Publications By Year¶
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- The publication list is automatically generated from bibtex by using bibtex2html.py that I developed.
- # denotes co-first authors. * denotes corresponding authors.
• Total Citations: 1773 • H-Index: 22
• TMI (5) • MedIA (2) • eLife (1) • NeuroImage (2) • HBM (5) • CC (1) • CVPR (1) • MICCAI (15) • IPMI (2) •
• 2025 • 2024 • 2023 • 2022 • 2021 • 2020 • 2018 • 2017 • 2016 • 2015 • 2014 • 2013 • 2012 • 2011 • 2010 • 2009 • 2008 •
2025
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Xiaolin Fan, Yan Wang, Yingying Zhang*, Mingkun Bao, Bosen Jia, Dong Lu, Yifan Gu, Jian Cheng*, Haogang Zhu*,
"AVP-AP: Self-supervised Automatic View Positioning in 3D cardiac CT via Atlas Prompting",
IEEE Transactions on Medical Imaging (TMI), 2025.![[pdf]](Icons/pdf.gif)
![[www]](Icons/www.gif)
[bibtex]
[abstract]
[arxiv]
Bibtex
@article{fan:AVP-AP:TMI2025, author = {Xiaolin Fan and Yan Wang and Yingying Zhang and Mingkun Bao and Bosen Jia and Dong Lu and Yifan Gu and Jian Cheng and Haogang Zhu}, doi = {10.1109/TMI.2025.3554785}, journal = {IEEE Transactions on Medical Imaging}, pdf = {https://arxiv.org/pdf/2504.05966.pdf}, publisher = {IEEE}, title = {AVP-AP: Self-supervised Automatic View Positioning in 3D cardiac CT via Atlas Prompting}, url = {https://dx.doi.org/10.1109/TMI.2025.3554785}, year = {2025} }Abstract
Automatic view positioning is crucial for cardiac computed tomography (CT) examinations, including disease diagnosis and surgical planning. However, it is highly challenging due to individual variability and large 3D search space. Existing work needs labor-intensive and time-consuming manual annotations to train view-specific models, which are limited to predicting only a fixed set of planes. However, in real clinical scenarios, the challenge of positioning semantic 2D slices with any orientation into varying coordinate space in arbitrary 3D volume remains unsolved. We thus introduce a novel framework, AVP-AP, the first to use Atlas Prompting for self-supervised Automatic View Positioning in the 3D CT volume. Specifically, this paper first proposes an atlas prompting method, which generates a 3D canonical atlas and trains a network to map slices into their corresponding positions in the atlas space via a self-supervised manner. Then, guided by atlas prompts corresponding to the given query images in a reference CT, we identify the coarse positions of slices in the target CT volume using rigid transformation between the 3D atlas and target CT volume, effectively reducing the search space. Finally, we refine the coarse positions by maximizing the similarity between the predicted slices and the query images in the feature space of a given foundation model. Our framework is flexible and efficient compared to other methods, outperforming other methods by 19.8% average structural similarity (SSIM) in arbitrary view positioning and achieving 9% SSIM in two-chamber view compared to four radiologists. Meanwhile, experiments on a public dataset validate our framework's generalizability.
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Shaodong Ding, Ziyang Liu, Pan Liu, Wanlin Zhu, Hong Xu, Zixiao Li, Haijun Niu, Jian Cheng*, Tao Liu*,
"C3R: Category contrastive adaptation and consistency regularization for cross-modality medical image segmentation",
Expert Systems with Applications, vol. 269, pp. 126304, 2025.
[bibtex]
[abstract]
Bibtex
@article{ding2025c3r, author = {Shaodong Ding and Ziyang Liu and Pan Liu and Wanlin Zhu and Hong Xu and Zixiao Li and Haijun Niu and Jian Cheng and Tao Liu}, doi = {h10.1016/j.eswa.2024.126304}, journal = {Expert Systems with Applications}, pages = {126304}, publisher = {Elsevier}, title = {C3R: Category contrastive adaptation and consistency regularization for cross-modality medical image segmentation}, url = {https://dx.doi.org/h10.1016/j.eswa.2024.126304}, volume = {269}, year = {2025} }Abstract
Unsupervised Domain Adaptation (UDA) has been widely used in cross- modality medical image segmentation, where the segmentation network is trained using both labeled images from the source domain and unlabeled images from the target domain. A prominent research direction in UDA involves learning domain-invariant features through image translation between the source and target domain images, typically utilizing cycle consistency loss and/or adversarial loss. However, we argue that the auxiliary cycle consistency loss and adversarial loss may interfere with the main segmentation loss during training, despite its utility in learning domain-invariant features. Furthermore, existing approaches in this research direction overlook the differentiation of various categories in domain-invariant feature learning. In this paper, we propose a novel UDA method, named C3R, for medical image segmentation. C3R mainly comprises four components: a shared encoder for learning domain- invariant features, a segmenter for generating segmentation output, and two decoders for image translation between the source and target domains. C3R fully explores consistency regularization in training, including image-level consistency, feature-level consistency, and segmentation output-level consistency. Moreover, C3R employs a detached training strategy to alleviate conflict between the main segmentation loss and auxiliary cycle consistency loss and adversarial loss. Last, C3R applies contrastive learning to pull together pixels of the same category, while pushing apart pixels of different categories, thereby enhancing the final segmentation results. Experimental results show that C3R outperforms other state- of-the-art methods by a considerable margin in Dice: 2.25% in cardiac substructure segmentation, 7.98% in brain tumor segmentation, and 1.8% in abdominal multi-organ segmentation.
2024
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Guozheng Feng, Yiwen Wang, Weijie Huang, Haojie Chen, Jian Cheng, Ni Shu*,
"Spatial and temporal pattern of structure–function coupling of human brain connectome with development",
eLife, vol. 13, pp. RP93325, jun, 2024.
[bibtex]
[abstract]
[citations: 12]
Bibtex
@article{Feng_elife2024, author = {Guozheng Feng and Yiwen Wang and Weijie Huang and Haojie Chen and Jian Cheng and Ni Shu}, doi = {10.7554/eLife.93325}, editor = {Huang, Susie Y and Roiser, Jonathan}, journal = {eLife}, month = {jun}, pages = {RP93325}, publisher = {eLife Sciences Publications, Ltd}, title = {Spatial and temporal pattern of structure–function coupling of human brain connectome with development}, url = {https://dx.doi.org/10.7554/eLife.93325}, volume = {13}, year = {2024} }Abstract
Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7–21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC–FC coupling. Our findings revealed that SC–FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC–FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC–FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC–FC coupling is positively associated with genes in oligodendrocyte- related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC–FC coupling in typical development.
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Yijun Zhou, Jing Jing, Zhe Zhang, Yuesong Pan, Xueli Cai, Wanlin Zhu, Zixiao Li, Chang Liu, Hao Liu, Xia Meng, Jian Cheng, Yilong Wang, Hao Li, Suying Wang, Haijun Niu, Wei Wen, Perminder S Sachdev, Tiemin Wei, Tao Liu*, Yongjun Wang*,
"Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study",
Human Brain Mapping (HBM), vol. 45, no. 2, pp. e26598, 2024.
[bibtex]
[abstract]
[citations: 1]
Bibtex
@article{zhou2024disrupted, author = {Yijun Zhou and Jing Jing and Zhe Zhang and Yuesong Pan and Xueli Cai and Wanlin Zhu and Zixiao Li and Chang Liu and Hao Liu and Xia Meng and Jian Cheng and Yilong Wang and Hao Li and Suying Wang and Haijun Niu and Wei Wen and Perminder S Sachdev and Tiemin Wei and Tao Liu and Yongjun Wang}, doi = {10.1002/hbm.26598}, journal = {Human Brain Mapping}, number = {2}, pages = {e26598}, publisher = {Wiley Online Library}, title = {Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study}, url = {https://dx.doi.org/10.1002/hbm.26598}, volume = {45}, year = {2024} }Abstract
The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called “rich-club” are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich- club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising
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Jianyu Li#, Jian Cheng#, Lei Yang#, Qihui Niu*, Yuanchao Zhang*, Lena Palaniyappan,
"Association of cortical gyrification, white matter microstructure, and phenotypic profile in medication-naïve obsessive–compulsive disorder",
Psychological Medicine, vol. 54, no. 8, pp. 1573–1579, 2024.
[bibtex]
[abstract]
[citations: 2]
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@article{li_cheng_yang_niu_zhang_palaniyappan_2023, author = {Jianyu Li and Jian Cheng and Lei Yang and Qihui Niu and Yuanchao Zhang and Lena Palaniyappan}, doi = {10.1017/S0033291723003422}, journal = {Psychological Medicine}, number = {8}, pages = {1573-1579}, publisher = {Cambridge University Press}, title = {Association of cortical gyrification, white matter microstructure, and phenotypic profile in medication-naïve obsessive–compulsive disorder}, url = {https://dx.doi.org/10.1017/S0033291723003422}, volume = {54}, year = {2024} }Abstract
Background Obsessive–compulsive disorder (OCD) is thought to arise from dysconnectivity among interlinked brain regions resulting in a wide spectrum of clinical manifestations. Cortical gyrification, a key morphological feature of human cerebral cortex, has been considered associated with developmental connectivity in early life. Monitoring cortical gyrification alterations may provide new insights into the developmental pathogenesis of OCD. Methods Sixty- two medication-naive patients with OCD and 59 healthy controls (HCs) were included in this study. Local gyrification index (LGI) was extracted from T1-weighted MRI data to identify the gyrification changes in OCD. Total distortion (splay, bend, or twist of fibers) was calculated using diffusion-weighted MRI data to examine the changes in white matter microstructure in patients with OCD. Results Compared with HCs, patients with OCD showed significantly increased LGI in bilateral medial frontal gyrus and the right precuneus, where the mean LGI was positively correlated with anxiety score. Patients with OCD also showed significantly decreased total distortion in the body, genu, and splenium of the corpus callosum (CC), where the average distortion was negatively correlated with anxiety scores. Intriguingly, the mean LGI of the affected cortical regions was significantly correlated with the mean distortion of the affected white matter tracts in patients with OCD. Conclusions We demonstrated associations among increased LGI, aberrant white matter geometry, and higher anxiety in patients with OCD. Our findings indicate that developmental dysconnectivity-driven alterations in cortical folding are one of the neural substrates underlying the clinical manifestations of OCD.
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Yuwen Wang, Mingxiu Han, Yudan Peng, Ruoqi Zhao, Dongqiong Fan, Xia Meng, Hong Xu, Haijun Niu, Jian Cheng*, Tao Liu*,
"LGNet: Learning local--global EEG representations for cognitive workload classification in simulated flights",
Biomedical Signal Processing and Control, vol. 92, pp. 106046, 2024.
[bibtex]
[abstract]
[citations: 11]
Bibtex
@article{wang2024lgnet, author = {Yuwen Wang and Mingxiu Han and Yudan Peng and Ruoqi Zhao and Dongqiong Fan and Xia Meng and Hong Xu and Haijun Niu and Jian Cheng and Tao Liu}, doi = {10.1016/j.bspc.2024.106046}, journal = {Biomedical Signal Processing and Control}, pages = {106046}, publisher = {Elsevier}, title = {LGNet: Learning local--global EEG representations for cognitive workload classification in simulated flights}, url = {https://dx.doi.org/10.1016/j.bspc.2024.106046}, volume = {92}, year = {2024} }Abstract
Cognitive workload assessment is crucial for ensuring pilots' safety during flights. Electroencephalography (EEG) is a promising tool for monitoring cognitive workload. Convolutional neural networks (CNNs) are effective in automatically extracting local EEG representations. However, CNNs have limitations in global representations, because a global representation in CNNs normally requires CNNs to be deep enough to have a global receptive field, which normally results in overfitting. To address this issue, we propose a local and global network (LGNet) for assessing two levels of cognitive workload based on EEG during simulated flight. We fuse convolutional and Transformer layers to extract local and global representations from the EEG signals. To enhance the learning performance, we propose a novel SCCE loss, which combines the supervised contrastive loss with the traditional cross-entropy loss. We collect 32-channel EEG data from 10 subjects who perform low and high cognitive workload flight tasks with passive auditory stimuli in a flight simulator on 3 separate days, with each participant performing the task for approximately 345 min. The results show that LGNet with the SCCE loss achieves a 4-fold average classification accuracy of 91.19 % based on cross-clip data partitioning and an average classification accuracy of 83.26 % based on cross-session data partitioning when no target session data is added to the training data. These results significantly outperform classifiers based on handcrafted features and state-of-the-art deep learning methods.
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Yuwen Wang#, Yudan Peng#, Mingxiu Han, Xinyi Liu, Haijun Niu, Jian Cheng*, Suhua Chang*, Tao Liu*,
"GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals",
Journal of Neural Engineering, vol. 21, no. 3, pp. 036042, jun, 2024.
[bibtex]
[abstract]
[citations: 4]
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@article{Wang_JNE2024, author = {Yuwen Wang and Yudan Peng and Mingxiu Han and Xinyi Liu and Haijun Niu and Jian Cheng and Suhua Chang and Tao Liu}, doi = {10.1088/1741-2552/ad5048}, journal = {Journal of Neural Engineering}, month = {jun}, number = {3}, pages = {036042}, publisher = {IOP Publishing}, title = {GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals}, url = {http://iopscience.iop.org/article/10.1088/1741-2552/ad5048}, volume = {21}, year = {2024} }Abstract
Objective. Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge. Approach. Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network (ResGCN) block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance. Main results. The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls (NC), in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross- validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve (AUC) of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks. Significance. The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical- assisted diagnosis.
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Keyi He, Bo Peng, Weibo Yu, Yan Liu, Surui Liu, Jian Cheng*, Yakang Dai*,
"A Novel Mis-Seg-Focus Loss Function Based on a Two-Stage nnU-Net Framework for Accurate Brain Tissue Segmentation",
Bioengineering, vol. 11, no. 5, pp. 427, 2024.
[bibtex]
[abstract]
Bibtex
@article{he2024novel, author = {Keyi He and Bo Peng and Weibo Yu and Yan Liu and Surui Liu and Jian Cheng and Yakang Dai}, doi = {10.3390/bioengineering11050427}, journal = {Bioengineering}, number = {5}, pages = {427}, publisher = {MDPI}, title = {A Novel Mis-Seg-Focus Loss Function Based on a Two-Stage nnU-Net Framework for Accurate Brain Tissue Segmentation}, url = {https://dx.doi.org/10.3390/bioengineering11050427}, volume = {11}, year = {2024} }Abstract
Brain tissue segmentation plays a critical role in the diagnosis, treatment, and study of brain diseases. Accurately identifying these boundaries is essential for improving segmentation accuracy. However, distinguishing boundaries between different brain tissues can be challenging, as they often overlap. Existing deep learning methods primarily calculate the overall segmentation results without adequately addressing local regions, leading to error propagation and mis-segmentation along boundaries. In this study, we propose a novel mis-segmentation-focused loss function based on a two-stage nnU-Net framework. Our approach aims to enhance the model’s ability to handle ambiguous boundaries and overlapping anatomical structures, thereby achieving more accurate brain tissue segmentation results. Specifically, the first stage targets the identification of mis-segmentation regions using a global loss function, while the second stage involves defining a mis- segmentation loss function to adaptively adjust the model, thus improving its capability to handle ambiguous boundaries and overlapping anatomical structures. Experimental evaluations on two datasets demonstrate that our proposed method outperforms existing approaches both quantitatively and qualitatively.
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Ximing Nie, Jinxu Yang, Xinxin Li, Tianming Zhan, Dongdong Liu, Hongyi Yan, Yufei Wei, Xiran Liu, Jiaping Chen, Guoyang Gong, Zhenzhou Wu, Zhonghua Yang, Miao Wen, Weibin Gu, Yuesong Pan, Yong Jiang, Xia Meng, Tao Liu, Jian Cheng, Zixiao Li, Zhongrong Miao, Liping Liu*,
"Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model",
Stroke and Vascular Neurology, pp. svn–2023, 2024.
[bibtex]
[abstract]
[citations: 2]
Bibtex
@article{nie2024prediction, author = {Ximing Nie and Jinxu Yang and Xinxin Li and Tianming Zhan and Dongdong Liu and Hongyi Yan and Yufei Wei and Xiran Liu and Jiaping Chen and Guoyang Gong and Zhenzhou Wu and Zhonghua Yang and Miao Wen and Weibin Gu and Yuesong Pan and Yong Jiang and Xia Meng and Tao Liu and Jian Cheng and Zixiao Li and Zhongrong Miao and Liping Liu}, doi = {10.1136/svn-2023-002500}, journal = {Stroke and Vascular Neurology}, pages = {svn-2023}, publisher = {BMJ Specialist Journals}, title = {Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model}, url = {https://dx.doi.org/10.1136/svn-2023-002500}, year = {2024} }Abstract
Background Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning method for predicting futile recanalisation. Methods Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management workflow. These models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following EVT. The optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation prediction. Results Using a hybrid feature selection approach, we trained and tested multiple classifiers on two independent patient cohorts (n=1122) to develop a hybrid machine learning-based prediction model. The model demonstrated superior discriminative ability compared with other models and scoring systems (area under the curve=0.80, 95% CI 0.73 to 0.87) and was transformed into a web application (RESCUE-FR Index) that provides a risk stratification system for individual prediction (accessible online at fr- index.biomind.cn/RESCUE-FR/). Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.
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Luyao Luo, Pan Liu, Wanxing Ye, Fengwei Chen, Yu Liu, Ziyang Liu, Jing Jing, Yunyun Xiong, Wanlin Zhu, Yong Jiang, Jian Cheng, Yongjun Wang, Tao Liu,
"CT perfusion parameter estimation in stroke using neural network with transformer and physical model priors",
Computers in Biology and Medicine, vol. 182, pp. 109134, 2024.
[bibtex]
[abstract]
Bibtex
@article{luo2024ct, author = {Luyao Luo and Pan Liu and Wanxing Ye and Fengwei Chen and Yu Liu and Ziyang Liu and Jing Jing and Yunyun Xiong and Wanlin Zhu and Yong Jiang and Jian Cheng and Yongjun Wang and Tao Liu}, doi = {10.1016/j.compbiomed.2024.109134}, journal = {Computers in Biology and Medicine}, pages = {109134}, publisher = {Elsevier}, title = {CT perfusion parameter estimation in stroke using neural network with transformer and physical model priors}, url = {https://dx.doi.org/10.1016/j.compbiomed.2024.109134}, volume = {182}, year = {2024} }Abstract
Objectives CT perfusion (CTP) imaging is vital in treating acute ischemic stroke by identifying salvageable tissue and the infarcted core. CTP images allow quantitative estimation of CT perfusion parameters, which can provide information on the degree of tissue hypoperfusion and its salvage potential. Traditional methods for estimating perfusion parameters, such as singular value decomposition (SVD) and its variations, are known to be sensitive to noise and inaccuracies in the arterial input function. To our knowledge, there has been no implementation of deep learning methods for CT perfusion parameter estimation. Materials & methods In this work, we propose a deep learning method based on the Transformer model, named CTPerformer-Net, for CT perfusion parameter estimation. In addition, our method incorporates some physical priors. We integrate physical consistency prior, smoothness prior and the physical model prior through the design of the loss function. We also generate a simulation dataset based on physical model prior for training the network model. Results In the simulation dataset, CTPerformer-Net exhibits a 23.4 % increase in correlation coefficients, a 95.2 % decrease in system error, and a 90.7 % reduction in random error when contrasted with block-circulant SVD. CTPerformer-Net successfully identifies hypoperfused and infarcted lesions in 103 real CTP images from the ISLES 2018 challenge dataset. It achieves a mean dice score of 0.36 for the infarct core segmentation, which is slightly higher than the commercially available software (dice coefficient: 0.34) used as a reference level by the challenge. Conclusion Experimental results on the simulation dataset demonstrate that CTPerformer-Net achieves better performance compared to block-circulant SVD. The real-world patient dataset confirms the validity of CTPerformer-Net.
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Si-Miao Zhang, Jing Wang, Yi-Xuan Wang, Tao Liu, Haogang Zhu, Han Zhang, Jian Cheng*,
"Mixed Integer Linear Programming for Discrete Sampling Scheme Design in Diffusion MRI",
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'24), pp. 313–322, 2024.
[bibtex]
Bibtex
@inproceedings{zhang:MICCAI2024mixed, author = {Si-Miao Zhang and Jing Wang and Yi-Xuan Wang and Tao Liu and Haogang Zhu and Han Zhang and Jian Cheng}, booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'24)}, doi = {10.1007/978-3-031-72069-7_30}, organization = {Springer}, pages = {313-322}, title = {Mixed Integer Linear Programming for Discrete Sampling Scheme Design in Diffusion MRI}, url = {https://dx.doi.org/10.1007/978-3-031-72069-7_30}, year = {2024} } -
Mingkun Bao, Yan Wang, Xinlong Wei, Bosen Jia, Xiaolin Fan, Dong Lu, Yifan Gu, Jian Cheng, Yingying Zhang*, Chuanyu Wang*, Haogang Zhu*,
"Real-World Visual Navigation for Cardiac Ultrasound View Planning",
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'24), pp. 317–326, 2024.
[bibtex]Bibtex
@inproceedings{bao:MICCAI2024real, author = {Mingkun Bao and Yan Wang and Xinlong Wei and Bosen Jia and Xiaolin Fan and Dong Lu and Yifan Gu and Jian Cheng and Yingying Zhang and Chuanyu Wang and Haogang Zhu}, booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'24)}, organization = {Springer}, pages = {317-326}, title = {Real-World Visual Navigation for Cardiac Ultrasound View Planning}, year = {2024} } -
Jing Yang, Jian Cheng*, Cheng Li, Wenxin Fan, Juan Zou, Ruoyou Wu, Shanshan Wang*,
"Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging",
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'24), 2024.
[bibtex]
[abstract]
[arxiv]
[citations: 1]
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@inproceedings{yang2024simultaneous, author = {Jing Yang and Jian Cheng and Cheng Li and Wenxin Fan and Juan Zou and Ruoyou Wu and Shanshan Wang}, booktitle = {IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'24)}, pdf = {https://arxiv.org/pdf/2401.01662.pdf}, title = {Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging}, url = {https://arxiv.org/abs/2401.01662}, year = {2024} }Abstract
Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the in vivo human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying l1-norm and total-variation regularization.The experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise
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Ruoyou Wu, Jian Cheng, Cheng Li, Juan Zou, Jing Yang, Wenxin Fan, Yong Liang, Shanshan Wang,
"CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-Assisted Implicit Neural Representation Learning",
MICCAI Workshop on Machine Learning in Medical Imaging (MLMI), pp. 114–123, 2024.
[bibtex]
[abstract]
[arxiv]
[citations: 3]
Bibtex
@inproceedings{wu2024csr, author = {Ruoyou Wu and Jian Cheng and Cheng Li and Juan Zou and Jing Yang and Wenxin Fan and Yong Liang and Shanshan Wang}, booktitle = {MICCAI Workshop on Machine Learning in Medical Imaging (MLMI)}, doi = {10.1007/978-3-031-73284-3_12}, organization = {Springer}, pages = {114-123}, pdf = {https://arxiv.org/pdf/2404.03209.pdf}, title = {CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-Assisted Implicit Neural Representation Learning}, url = {https://dx.doi.org/10.1007/978-3-031-73284-3_12}, year = {2024} }Abstract
Deep learning-based dMRI super-resolution methods can effectively enhance image resolution by leveraging the learning capabilities of neural networks on large datasets. However, these methods tend to learn a fixed scale mapping between low-resolution (LR) and high- resolution (HR) images, overlooking the need for radiologists to scale the images at arbitrary resolutions. Moreover, the pixel-wise loss in the image domain tends to generate over-smoothed results, losing fine textures and edge information. To address these issues, we propose a novel continuous super-resolution method for dMRI, called CSR-dMRI, which utilizes an anatomical structure-assisted implicit neural representation learning approach. Specifically, the CSR-dMRI model consists of two components. The first is the latent feature extractor, which primarily extracts latent space feature maps from LR dMRI and anatomical images while learning structural prior information from the anatomical images. The second is the implicit function network, which utilizes voxel coordinates and latent feature vectors to generate voxel intensities at corresponding positions. Additionally, a frequency-domain-based loss is introduced to preserve the structural and texture information, further enhancing the image quality. Extensive experiments on the publicly available HCP dataset validate the effectiveness of our approach. Furthermore, our method demonstrates superior generalization capability and can be applied to arbitrary-scale super-resolution, including non-integer scale factors, expanding its applicability beyond conventional approaches.
2023
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Yan Wang, Jian Cheng*, Yixin Chen, Shuai Shao, Lanyun Zhu, Zhenzhou Wu, Tao Liu, Haogang Zhu*,
"FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation",
IEEE Transactions on Medical Imaging (TMI), vol. 42, no. 12, pp. 3738–3751, 2023.
[bibtex]
[abstract]
[arxiv]
[citations: 33]
Bibtex
@article{wang2023fvp, author = {Yan Wang and Jian Cheng and Yixin Chen and Shuai Shao and Lanyun Zhu and Zhenzhou Wu and Tao Liu and Haogang Zhu}, doi = {10.1109/TMI.2023.3306105}, journal = {IEEE Transactions on Medical Imaging}, number = {12}, pages = {3738-3751}, pdf = {https://arxiv.org/pdf/2304.13672.pdf}, title = {FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation}, url = {https://dx.doi.org/10.1109/TMI.2023.3306105}, volume = {42}, year = {2023} }Abstract
Medical image segmentation methods normally perform poorly when there is a domain shift between training and testing data. Unsupervised Domain Adaptation (UDA) addresses the domain shift problem by training the model using both labeled data from the source domain and unlabeled data from the target domain. Source-Free UDA (SFUDA) was recently proposed for UDA without requiring the source data during the adaptation, due to data privacy or data transmission issues, which normally adapts the pre-trained deep model in the testing stage. However, in real clinical scenarios of medical image segmentation, the trained model is normally frozen in the testing stage. In this paper, we propose Fourier Visual Prompting (FVP) for SFUDA of medical image segmentation. Inspired by prompting learning in natural language processing, FVP steers the frozen pre-trained model to perform well in the target domain by adding a visual prompt to the input target data. In FVP, the visual prompt is parameterized using only a small amount of low-frequency learnable parameters in the input frequency space, and is learned by minimizing the segmentation loss between the predicted segmentation of the prompted target image and reliable pseudo segmentation label of the target image under the frozen model. To our knowledge, FVP is the first work to apply visual prompts to SFUDA for medical image segmentation. The proposed FVP is validated using three public datasets, and experiments demonstrate that FVP yields better segmentation results, compared with various existing methods.
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Haichao Zhao, Wei Wen, Jian Cheng*, Jiyang Jiang, Nicole Kochan, Haijun Niu, Henry Brodaty, Perminder Sachdev, Tao Liu*,
"An accelerated degeneration of white matter microstructure and networks in the nondemented old–old",
Cerebral Cortex (CC), vol. 33, no. 8, pp. 4688–4698, 2023.
[bibtex]
[abstract]
[citations: 8]
Bibtex
@article{zhao:DFA:CC2022, author = {Haichao Zhao and Wei Wen and Jian Cheng and Jiyang Jiang and Nicole Kochan and Haijun Niu and Henry Brodaty and Perminder Sachdev and Tao Liu}, doi = {10.1093/cercor/bhac372}, journal = {Cerebral Cortex}, number = {8}, pages = {4688-4698}, pdf = {https://academic.oup.com/cercor/advance-article-pdf/doi/10.1093/cercor/bhac372/46212258/bhac372.pdf}, publisher = {Oxford University Press}, title = {An accelerated degeneration of white matter microstructure and networks in the nondemented old–old}, url = {https://doi.org/10.1093/cercor/bhac372}, volume = {33}, year = {2023} }Abstract
{The nondemented old–old over the age of 80 comprise a rapidly increasing population group; they can be regarded as exemplars of successful aging. However, our current understanding of successful aging in advanced age and its neural underpinnings is limited. In this study, we measured the microstructural and network-based topological properties of brain white matter using diffusion- weighted imaging scans of 419 community-dwelling nondemented older participants. The participants were further divided into 230 young–old (between 72 and 79, mean = 76.25 ± 2.00) and 219 old–old (between 80 and 92, mean = 83.98 ± 2.97). Results showed that white matter connectivity in microstructure and brain networks significantly declined with increased age and that the declined rates were faster in the old–old compared with young–old. Mediation models indicated that cognitive decline was in part through the age effect on the white matter connectivity in the old–old but not in the young–old. Machine learning predictive models further supported the crucial role of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population. Our findings shed new light on white matter connectivity in the nondemented aging brains and may contribute to uncovering the neural substrates of successful brain aging.} -
Jing Jing, Chang Liu, Wanlin Zhu, Yuesong Pan, Jiyang Jiang, Xueli Cai, Zhe Zhang, Zixiao Li, Yijun Zhou, Xia Meng, Jian Cheng, Yilong Wang, Hao Li, Yong Jiang, Huaguang Zheng, Suying Wang, Haijun Niu, Wei Wen, Perminder S. Sachdev, Tiemin Wei, Tao Liu*, Yongjun Wang*,
"Increased Resting-State Functional Connectivity as a Compensatory Mechanism for Reduced Brain Volume in Prediabetes and Type 2 Diabetes",
Diabetes Care, vol. 46, no. 4, pp. 819–827, 02, 2023.
[bibtex]
[abstract]
[citations: 13]
Bibtex
@article{jing2023increased, author = {Jing Jing and Chang Liu and Wanlin Zhu and Yuesong Pan and Jiyang Jiang and Xueli Cai and Zhe Zhang and Zixiao Li and Yijun Zhou and Xia Meng and Jian Cheng and Yilong Wang and Hao Li and Yong Jiang and Huaguang Zheng and Suying Wang and Haijun Niu and Wei Wen and Perminder S. Sachdev and Tiemin Wei and Tao Liu and Yongjun Wang}, doi = {10.2337/dc22-1998}, journal = {Diabetes Care}, month = {02}, number = {4}, pages = {819-827}, publisher = {Am Diabetes Assoc}, title = {Increased Resting-State Functional Connectivity as a Compensatory Mechanism for Reduced Brain Volume in Prediabetes and Type 2 Diabetes}, url = {https://doi.org/10.2337/dc22-1998}, volume = {46}, year = {2023} }Abstract
{To investigate the contribution of alterations in brain structure and function to cognitive function and their interactions in individuals with diabetes and patients with type 2 diabetes mellitus (T2DM).This population-based study included 2,483 participants who underwent structural MRI (n = 569 with normal glucose metabolism [NGM], n = 1,353 with prediabetes, and n = 561 with T2DM) and cognitive testing. Of these, 2145 participants also underwent functional MRI (n = 496 NGM, n = 1,170 prediabetes, and n = 479 T2DM). Multivariate linear regression models were used to assess the association of brain volume and functional connectivity with cognition, as well as the association of brain volume and functional connectivity.Compared with NGM participants, those with T2DM had lower brain volume in a wide range of brain regions and stronger functional connectivity between the bilateral thalamus and brain functional network (visual network and default mode network), and those with prediabetes had lower brain volume in specific local regions (subcortical gray matter volume and subcortical subregions [bilateral thalamus, bilateral nucleus accumbens, and right putamen]) and stronger functional connectivity between the right thalamus and visual network. Cognition was associated with greater right thalamus volume and lower functional connectivity between the right thalamus and visual network. Functional connectivity between the right thalamus and visual network was associated with lower right thalamus volume.Cognition was associated with greater brain volume and lower functional connectivity in T2DM. Increased functional connectivity may indicate a compensatory mechanism for reduced brain volume that begins in the prediabetic phase.} -
Yingying Zhang, Haogang Zhu*, Jian Cheng, Jingyi Wang, Xiaoyan Gu, Jiancheng Han, Ye Zhang, Ying Zhao, Yihua He*, Hongjia Zhang*,
"Improving the Quality of Fetal Heart Ultrasound Imaging with Multihead Enhanced Self-Attention and Contrastive Learning",
IEEE Journal of Biomedical and Health Informatics (JBHI), 2023.
[bibtex]
[abstract]
[citations: 7]
Bibtex
@article{zhang2023improving, author = {Yingying Zhang and Haogang Zhu and Jian Cheng and Jingyi Wang and Xiaoyan Gu and Jiancheng Han and Ye Zhang and Ying Zhao and Yihua He and Hongjia Zhang}, doi = {10.1109/JBHI.2023.3303573}, journal = {IEEE Journal of Biomedical and Health Informatics}, pdf = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10213997}, publisher = {IEEE}, title = {Improving the Quality of Fetal Heart Ultrasound Imaging with Multihead Enhanced Self-Attention and Contrastive Learning}, url = {https://dx.doi.org/10.1109/JBHI.2023.3303573}, year = {2023} }Abstract
Fetal congenital heart disease (FCHD) is a common, serious birth defect affecting ∼1% of newborns annually. Fetal echocardiography is the most effective and important technique for prenatal FCHD diagnosis. The prerequisites for accurate ultrasound FCHD diagnosis are accurate view recognition and high-quality diagnostic view extraction. However, these manual clinical procedures have drawbacks such as, varying technical capabilities and inefficiency. Therefore, the automatic identification of high-quality multiview fetal heart scan images is highly desirable to improve prenatal diagnosis efficiency and accuracy of FCHD. Here, we present a framework for multiview fetal heart ultrasound image recognition and quality assessment that comprises two parts: a multiview classification and localization network (MCLN) and an improved contrastive learning network (ICLN). In the MCLN, a multihead enhanced self-attention mechanism is applied to construct the classification network and identify six accurate and interpretable views of the fetal heart. In the ICLN, anatomical structure standardization and image clarity are considered. With contrastive learning, the absolute loss, feature relative loss and predicted value relative loss are combined to achieve favorable quality assessment results. Experiments show that the MCLN outperforms other state-of-the-art networks by 1.52–13.61% when determining the F1 score in six standard view recognition tasks, and the ICLN is comparable to the performance of expert cardiologists in the quality assessment of fetal heart ultrasound images, reaching 97% on a test set within 2 points for the four- chamber view task. Thus, our architecture offers great potential in helping cardiologists improve quality control for fetal echocardiographic images in clinical practice.
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Ke Fang, Zejun Wang, Qi Xia, Yingchao Liu, Bao Wang, Zhaowei Cheng, Jian Cheng, Xinyu Jin*, Ruiliang Bai*, Lanjuan Li*,
"Normalizing Flow-based Distribution Estimation of Pharmacokinetic Parameters in Dynamic Contrast-Enhanced Magnetic Resonance Imaging",
IEEE Transactions on Biomedical Engineering (TBME), 2023.
[bibtex]
[abstract]
[citations: 2]
Bibtex
@article{fang2023normalizing, author = {Ke Fang and Zejun Wang and Qi Xia and Yingchao Liu and Bao Wang and Zhaowei Cheng and Jian Cheng and Xinyu Jin and Ruiliang Bai and Lanjuan Li}, doi = {10.1109/TBME.2023.3318087}, journal = {IEEE Transactions on Biomedical Engineering}, publisher = {IEEE}, title = {Normalizing Flow-based Distribution Estimation of Pharmacokinetic Parameters in Dynamic Contrast-Enhanced Magnetic Resonance Imaging}, url = {https://dx.doi.org/10.1109/TBME.2023.3318087}, year = {2023} }Abstract
Objective: The pharmacokinetic (PK) parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide valuable information for clinical research and diagnosis. However, these estimated PK parameters suffer from many sources of variability. Thus, the estimation of the posterior distributions of these PK parameters could provide a way to simultaneously quantify the values and uncertainties of the PK parameters. Our objective is to develop an efficient and flexible method to more closely approximate and estimate the underlying posterior distributions of the PK parameters. Methods: The normalizing flow model-based parameters distribution estimation neural network (FPDEN) is proposed to adaptively learn and estimate the posterior distributions of the PK parameters. The maximum likelihood estimation (MLE) loss is directly constructed based on the parameter distributions learned by the normalizing flow model, rather than pre-defined distributions. Results: Experimental analysis shows that the proposed method can improve parameter estimation accuracy. Moreover, the uncertainty derived from the parameter distribution constitutes an effective indicator to exclude unreliable parametric results. A successful demonstration is the improved classification performance of the glioma World Health Organization (WHO) grading task, specifically in terms of distinguishing between low and high grades, as well as between Grade III and Grade IV. Conclusion: The FPDEN method offers improved accuracy for estimation of PK parameters and boosts the performance of the glioma grading task. Significance: By enhancing the precision and reliability of DCE-MRI, the proposed method promotes its further applications in clinical practice.
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Ruoqi Zhao, Yuwen Wang, Xiangxin Cheng, Wanlin Zhu, Xia Meng, Haijun Niu, Jian Cheng*, Tao Liu*,
"A mutli-scale spatial-temporal convolutional neural network with contrastive learning for motor imagery EEG classification",
Medicine in Novel Technology and Devices, vol. 17, pp. 100215, 2023.
[bibtex]
[abstract]
[citations: 13]
Bibtex
@article{ZHAO2023100215, author = {Ruoqi Zhao and Yuwen Wang and Xiangxin Cheng and Wanlin Zhu and Xia Meng and Haijun Niu and Jian Cheng and Tao Liu}, doi = {https://doi.org/10.1016/j.medntd.2023.100215}, journal = {Medicine in Novel Technology and Devices}, pages = {100215}, title = {A mutli-scale spatial-temporal convolutional neural network with contrastive learning for motor imagery EEG classification}, url = {https://www.sciencedirect.com/science/article/pii/S2590093523000103}, volume = {17}, year = {2023} }Abstract
Motor imagery (MI) based Brain-computer interfaces (BCIs) have a wide range of applications in the stroke rehabilitation field. However, due to the low signal-to-noise ratio and high cross-subject variation of the electroencephalogram (EEG) signals generated by motor imagery, the classification performance of the existing methods still needs to be improved to meet the need of real practice. To overcome this problem, we propose a multi-scale spatial-temporal convolutional neural network called MSCNet. We introduce the contrastive learning into a multi-temporal convolution scale backbone to further improve the robustness and discrimination of embedding vectors. Experimental results of binary classification show that MSCNet outperforms the state-of-the-art methods, achieving accuracy improvement of 6.04%, 3.98%, and 8.15% on BCIC IV 2a, SMR- BCI, and OpenBMI datasets in subject-dependent manner, respectively. The results show that the contrastive learning method can significantly improve the classification accuracy of motor imagery EEG signals, which provides an important reference for the design of motor imagery classification algorithms.
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Jing Jing, Ziyang Liu, Hao Guan, Wanlin Zhu, Zhe Zhang, Xia Meng, Jian Cheng, Yuesong Pan, Yong Jiang, Yilong Wang, Haijun Niu, Xingquan Zhao, Wei Wen, Jinxi Lin, Wei Li, Hao Li, Perminder S. Sachdev, Tao Liu, Zixiao Li, Dacheng Tao, Yongjun Wang,
"A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke",
Advanced Intelligent Systems, vol. 5, no. 4, pp. 2200240, 2023.
[bibtex]
[abstract]
[citations: 7]
Bibtex
@article{jing2023_AIS, author = {Jing Jing and Ziyang Liu and Hao Guan and Wanlin Zhu and Zhe Zhang and Xia Meng and Jian Cheng and Yuesong Pan and Yong Jiang and Yilong Wang and Haijun Niu and Xingquan Zhao and Wei Wen and Jinxi Lin and Wei Li and Hao Li and Perminder S. Sachdev and Tao Liu and Zixiao Li and Dacheng Tao and Yongjun Wang}, doi = {https://doi.org/10.1002/aisy.202200240}, journal = {Advanced Intelligent Systems}, number = {4}, pages = {2200240}, title = {A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aisy.202200240}, volume = {5}, year = {2023} }Abstract
Ischemic strokes (IS) and transient ischemic attacks (TIA) account for approximately 80\% of all strokes and are leading causes of death worldwide. Assessing the risk of recurrence or functional impairment in IS and TIA patients is essential to both acute phase treatment and secondary prevention. Current risk prediction systems that rely on clinical parameters alone without leveraging imaging data have only modest performance. Herein, a deep learning-based risk prediction system (RPS) is developed to predict the probability of stroke recurrence or disability (i.e., deep-learning stroke recurrence risk score, SRR score). Then, Kaplan–Meier analysis to evaluate the ability of SRR score to stratify patients at stroke recurrence risk is discussed. Using 15 166 Third China National Stroke Registry (CNSR-III) cases, the RPS's receiver operating characteristic curve (AUC) values of 0.850 for 14 day TIA recurrence prediction and 0.837 for 3 month IS disability prediction are used. Among patients deemed high risk by SRR score, 22.9\% and 24.4\% of individuals with TIA and IS respectively have stroke recurrence within 1 year, which are significantly higher than the rates in low- risk individuals. Deep learning-based RPS can outperform conventional risk scores and has the potential to assist accurate prognostication in stroke patients to optimize management.
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Yuxin Gong, Haogang Zhu*, Jixing Li, Jingchun Yang*, Jian Cheng, Ying Chang, Xiao Bai, Xunming Ji,
"SCCNet: Self-correction boundary preservation with a dynamic class prior filter for high-variability ultrasound image segmentation",
Computerized Medical Imaging and Graphics, vol. 104, pp. 102183, 2023.
[bibtex]
[abstract]
[citations: 5]
Bibtex
@article{gong2023sccnet, author = {Yuxin Gong and Haogang Zhu and Jixing Li and Jingchun Yang and Jian Cheng and Ying Chang and Xiao Bai and Xunming Ji}, doi = {10.1016/j.compmedimag.2023.102183}, journal = {Computerized Medical Imaging and Graphics}, pages = {102183}, publisher = {Elsevier}, title = {SCCNet: Self-correction boundary preservation with a dynamic class prior filter for high-variability ultrasound image segmentation}, url = {https://dx.doi.org/10.1016/j.compmedimag.2023.102183}, volume = {104}, year = {2023} }Abstract
The highly ambiguous nature of boundaries and similar objects is difficult to address in some ultrasound image segmentation tasks, such as neck muscle segmentation, leading to unsatisfactory performance. Thus, this paper proposes a two-stage network called SCCNet (self-correction context network) using a self-correction boundary preservation module and class-context filter to alleviate these problems. The proposed self-correction boundary preservation module uses a dynamic key boundary point (KBP) map to increase the capability of iteratively discriminating ambiguous boundary points segments, and the predicted segmentation map from one stage is used to obtain a dynamic class prior filter to improve the segmentation performance at Stage 2. Finally, three datasets, Neck Muscle, CAMUS and Thyroid, are used to demonstrate that our proposed SCCNet outperforms other state-of-the art methods, such as BPBnet, DSNnet, and RAGCnet. Our proposed network shows at least a 1.2–3.7% improvement on the three datasets, Neck Muscle, Thyroid, and CAMUS. The source code is available at https://github.com/lijixing0425/SCCNet.
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Guozheng Feng, Rui Chen, Rui Zhao, Yuanyuan Li, Leilei Ma, Yanpei Wang, Weiwei Men, Jiahong Gao, Shuping Tan, Jian Cheng, Yong He, Shaozheng Qin, Qi Dong, Sha Tao*, Ni Shu*,
"Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture",
Communications Biology, vol. 6, no. 1, pp. 1257, 2023.
[bibtex]
[abstract]
[citations: 4]
Bibtex
@article{feng2023longitudinal, author = {Guozheng Feng and Rui Chen and Rui Zhao and Yuanyuan Li and Leilei Ma and Yanpei Wang and Weiwei Men and Jiahong Gao and Shuping Tan and Jian Cheng and Yong He and Shaozheng Qin and Qi Dong and Sha Tao and Ni Shu}, doi = {10.1038/s42003-024-05771-z}, journal = {Communications Biology}, number = {1}, pages = {1257}, publisher = {Nature Publishing Group UK London}, title = {Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture}, url = {https://dx.doi.org/10.1038/s42003-024-05771-z}, volume = {6}, year = {2023} }Abstract
From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.
2022
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Shengke Xue#, Zhaowei Cheng#, Guangxu Han, Chaoliang Sun, Ke Fang, Yingchao Liu, Jian Cheng, Xinyu Jin, Ruiliang Bai*,
"2D probabilistic undersampling pattern optimization for MR image reconstruction",
Medical Image Analysis (MedIA), vol. 77, pp. 102346, 2022.
[bibtex]
[abstract]
[citations: 10]
Bibtex
@article{xue:MIA2022:sampling, author = {Shengke Xue and Zhaowei Cheng and Guangxu Han and Chaoliang Sun and Ke Fang and Yingchao Liu and Jian Cheng and Xinyu Jin and Ruiliang Bai}, doi = {https://doi.org/10.1016/j.media.2021.102346}, journal = {Medical Image Analysis}, pages = {102346}, title = {2D probabilistic undersampling pattern optimization for MR image reconstruction}, url = {https://www.sciencedirect.com/science/article/pii/S1361841521003911}, volume = {77}, year = {2022} }Abstract
With 3D magnetic resonance imaging (MRI), a tradeoff exists between higher image quality and shorter scan time. One way to solve this problem is to reconstruct high-quality MRI images from undersampled k-space. There have been many recent studies exploring effective k-space undersampling patterns and designing MRI reconstruction methods from undersampled k-space, which are two necessary steps. Most studies separately considered these two steps, although in theory, their performance is dependent on each other. In this study, we propose a joint optimization model, trained end-to-end, to simultaneously optimize the undersampling pattern in the Fourier domain and the reconstruction model in the image domain. A 2D probabilistic undersampling layer was designed to optimize the undersampling pattern and probability distribution in a differentiable manner. A 2D inverse Fourier transform layer was implemented to connect the Fourier domain and the image domain during the forward and back propagation. Finally, we discovered an optimized relationship between the probability distribution of the undersampling pattern and its corresponding sampling rate. Further testing was performed using 3D T1-weighted MR images of the brain from the MICCAI 2013 Grand Challenge on Multi-Atlas Labeling dataset and locally acquired brain 3D T1-weighted MR images of healthy volunteers and contrast-enhanced 3D T1-weighted MR images of high- grade glioma patients. The results showed that the recovered MR images using our 2D probabilistic undersampling pattern (with or without the reconstruction network) significantly outperformed those using the existing start-of-the-art undersampling strategies for both qualitative and quantitative comparison, suggesting the advantages and some extent of the generalization of our proposed method.
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Jing Jing#, Yijun Zhou#, Yuesong Pan#, Xueli Cai#, Wanlin Zhu, Zhe Zhang, Zixiao Li, Chang Liu, Xia Meng, Jian Cheng, Yilong Wang, Hao Li, Zhenzhou Wu, Suying Wang, Haijun Niu, Wei Wen, Tao Liu*, Tiemin Wei*, Yongjun Wang*, Perminder S. Sachdev,
"Reduced white matter microstructural integrity in prediabetes and diabetes: A population-based study",
eBioMedicine, vol. 82, pp. 104144, 2022.
[bibtex]
[abstract]
[citations: 17]
Bibtex
@article{jing:eBioMedicine2022, author = {Jing Jing and Yijun Zhou and Yuesong Pan and Xueli Cai and Wanlin Zhu and Zhe Zhang and Zixiao Li and Chang Liu and Xia Meng and Jian Cheng and Yilong Wang and Hao Li and Zhenzhou Wu and Suying Wang and Haijun Niu and Wei Wen and Tao Liu and Tiemin Wei and Yongjun Wang and Perminder S. Sachdev}, doi = {https://doi.org/10.1016/j.ebiom.2022.104144}, journal = {eBioMedicine}, pages = {104144}, pdf = {https://www.sciencedirect.com/science/article/pii/S2352396422003255/pdfft?md5=cd11719d27845497ef250123b8479b1f&pid=1-s2.0-S2352396422003255-main.pdf}, title = {Reduced white matter microstructural integrity in prediabetes and diabetes: A population-based study}, url = {https://www.sciencedirect.com/science/article/pii/S2352396422003255}, volume = {82}, year = {2022} }Abstract
Summary Background White matter (WM) microstructural abnormalities have been observed in diabetes. However, evidence of prediabetes is currently lacking. This study aims to investigate the WM integrity in prediabetes and diabetes. We also assess the association of WM abnormalities with glucose metabolism status and continuous glucose measures. Methods The WM integrity was analyzed using cross- sectional baseline data from a population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study. The cohort, including a total of 2218 cases with the mean age of 61.3 ± 6.6 years and 54.1% female, consisted of 1205 prediabetes which are categorized into two subgroups (a group of 254 prediabetes with combined impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) and the other group of 951 prediabetes without combined IFG/IGT), 504 diabetes, and 509 normal control subjects. Alterations of WM integrity were determined by diffusion tensor imaging along with tract-based spatial statistics analysis to compare diffusion metrics on WM skeletons between groups. The mixed- effects multivariate linear regression models were used to assess the association between WM microstructural alterations and glucose status. Findings Microstructural abnormalities distributed in local WM tracts in prediabetes with combined IFG/IGT and spread widely in diabetes. These WM abnormalities are associated with higher glucose measures. Interpretation Our findings suggest that WM microstructural abnormalities are already present at the prediabetes with combined IFG/IGT stage. Preventative strategies should begin early to maintain normal glucose metabolism and avert further destruction of WM integrity. Funding Partially supported by National Key R&D Program of China (2016YFC0901002).
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Hua Zhu, Lijun Zuo, Wanlin Zhu, Jing Jing, Zhe Zhang, Lingling Ding, Fengjuan Wang, Jian Cheng, Zhenzhou Wu, Yongjun Wang, Tao Liu*, Zixiao Li*,
"The distinct disrupted plasticity in structural and functional network in mild stroke with basal ganglia region infarcts",
Brain Imaging and Behavior, pp. 1–21, 2022.
[bibtex]
[abstract]
[citations: 13]
Bibtex
@article{zhu2022distinct, author = {Hua Zhu and Lijun Zuo and Wanlin Zhu and Jing Jing and Zhe Zhang and Lingling Ding and Fengjuan Wang and Jian Cheng and Zhenzhou Wu and Yongjun Wang and Tao Liu and Zixiao Li}, doi = {10.1007/s11682-022-00689-8}, journal = {Brain Imaging and Behavior}, pages = {1-21}, publisher = {Springer}, title = {The distinct disrupted plasticity in structural and functional network in mild stroke with basal ganglia region infarcts}, url = {https://dx.doi.org/10.1007/s11682-022-00689-8}, year = {2022} }Abstract
Stroke induced by basal ganglia infarction often impair cognitive function. The exploration of topological patterns in structural and functional networks associated cognitive impairment after stroke may contribute to understand the pathological mechanism of cognitive impairment caused by stroke. In this paper, graph theory analysis was applied to diffusion-weighted imaging (DWI) data and resting- state functional MRI (fMRI) data from 23 post-stroke patients with cognitive impairment (PSCI), 17 post-stroke patients without cognitive impairment (NPSCI), and 29 healthy controls (HC). Structural and functional connectivity between 90 cortical and subcortical brain regions was estimated and set threshold to construct a set of undirected graphs. Network-based statistics (NBS) was used to characterize altered connectivity patterns among the three groups. Compared to HC, the PSCI group demonstrated substantial reductions in all three types of connections—rich club, feeder, and local—in structural and functional networks. Specifically, in structural network analysis, reduced connections were observed within basal ganglia and basal ganglia-frontal networks, whereas in the functional network analysis, reduced connections were observed in fronto-parietal network (FPN) and cingulo-opercular networks (CON). Meanwhile, compared to HC, the NPSCI group demonstrated reductions in both feeder and local connections only within occipital area and occipital-temporal structural networks. The findings of reduced structural connectivity in regions stemming from a basal ganglia core and reduced functional connectivity in FPN and CON may indicate a bottom-up cognitive impairment induced by stroke. Graph analysis and connectomics may aid clinical diagnosis and serve as potential imaging biomarkers for post-stroke patients with cognitive impairment.
2021
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Jian Cheng#, Ziyang Liu#, Hao Guan, Zhenzhou Wu, Haogang Zhu, Jiyang Jiang, Wei Wen, Dacheng Tao, Tao Liu*,
"Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss",
IEEE Transactions on Medical Imaging (TMI), vol. 40, no. 12, pp. 3400–3412, 2021.
[bibtex]
[abstract]
[arxiv]
[code]
[citations: 75]
(Jian Cheng and Ziyang Liu contributed equally.)
Bibtex
@article{cheng:TMI2021, author = {Jian Cheng and Ziyang Liu and Hao Guan and Zhenzhou Wu and Haogang Zhu and Jiyang Jiang and Wei Wen and Dacheng Tao and Tao Liu}, doi = {10.1109/TMI.2021.3085948}, journal = {IEEE Transactions on Medical Imaging}, number = {12}, pages = {3400-3412}, pdf = {https://arxiv.org/pdf/2106.03052.pdf}, publisher = {IEEE}, title = {Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss}, url = {https://dx.doi.org/10.1109/TMI.2021.3085948}, volume = {40}, year = {2021} }Abstract
Chronological age of healthy people is able to be predicted accurately using deep neural networks from neuroimaging data, and the predicted brain age could serve as a biomarker for detecting aging-related diseases. In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Compared with existing methods, TSAN has the following improvements. First, TSAN uses a two-stage cascade network architecture, where the first-stage network estimates a rough brain age, then the second-stage network estimates the brain age more accurately from the discretized brain age by the first-stage network. Second, to our knowledge, TSAN is the first work to apply novel ranking losses in brain age estimation, together with the traditional mean square error (MSE) loss. Third, densely connected paths are used to combine feature maps with different scales. The experiments with $6586$ MRIs showed that TSAN could provide accurate brain age estimation, yielding mean absolute error (MAE) of $2.428$ and Pearson's correlation coefficient (PCC) of $0.985$, between the estimated and chronological ages. Furthermore, using the brain age gap between brain age and chronological age as a biomarker, Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) can be distinguished from healthy control (HC) subjects by support vector machine (SVM). Classification AUC in AD/HC and MCI/HC was $0.904$ and $0.823$, respectively. It showed that brain age gap is an effective biomarker associated with risk of dementia, and has potential for early-stage dementia risk screening. The codes and trained models have been released on GitHub: https://github.com/Milan-BUAA/TSAN- brain-age-estimation.
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Hui Tang, Tao Liu*, Hao Liu, Jiyang Jiang, Jian Cheng, Haijun Niu, Shuyu Li, Henry Brodaty, Perminder Sachdev, Wei Wen,
"A slower rate of sulcal widening in the brains of the nondemented oldest old",
NeuroImage, vol. 229, pp. 117740, 2021.
[bibtex]
[abstract]
[citations: 16]
Bibtex
@article{tang:NI2021, author = {Hui Tang and Tao Liu and Hao Liu and Jiyang Jiang and Jian Cheng and Haijun Niu and Shuyu Li and Henry Brodaty and Perminder Sachdev and Wei Wen}, doi = {10.1016/j.neuroimage.2021.117740}, journal = {NeuroImage}, pages = {117740}, pdf = {https://www.sciencedirect.com/science/article/pii/S1053811921000173/pdfft?md5=21c91728b2c65b222185ad07040f3a6f&pid=1-s2.0-S1053811921000173-main.pdf}, publisher = {Elsevier}, title = {A slower rate of sulcal widening in the brains of the nondemented oldest old}, url = {https://dx.doi.org/10.1016/j.neuroimage.2021.117740}, volume = {229}, year = {2021} }Abstract
The relationships between aging and brain morphology have been reported in many previous structural brain studies. However, the trajectories of successful brain aging in the extremely old remain underexplored. In the limited research on the oldest old, covering individuals aged 85 years and older, there are very few studies that have focused on the cortical morphology, especially cortical sulcal features. In this paper, we measured sulcal width and depth as well as cortical thickness from T1-weighted scans of 290 nondemented community-dwelling participants aged between 76 and 103 years. We divided the participants into young old (between 76 and 84; mean = 80.35±2.44; male/female = 76/88) and oldest old (between 85 and 103; mean = 91.74±5.11; male/female = 60/66) groups. The results showed that most of the examined sulci significantly widened with increased age and that the rates of sulcal widening were lower in the oldest old. The spatial pattern of the cortical thinning partly corresponded with that of sulcal widening. Compared to females, males had significantly wider sulci, especially in the oldest old. This study builds a foundation for future investigations of neurocognitive disorders and neurodegenerative diseases in the oldest old, including centenarians.
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Haichao Zhao#, Jian Cheng#, Tao Liu*, Jiyang Jiang, Forrest Koch, Perminder S. Sachdev, Peter J. Basser, Wei Wen, for the Alzheimer's Disease Neuroimaging Initiative,
"Orientational changes of white matter fibers in Alzheimer's disease and amnestic mild cognitive impairment",
Human Brain Mapping (HBM), 2021.
[bibtex]
[abstract]
[citations: 14]
(Haichao Zhao and Jian Cheng contributed equally.)
Bibtex
@article{zhao:DFA:HBM2021, author = {Haichao Zhao and Jian Cheng and Tao Liu and Jiyang Jiang and Forrest Koch and Perminder S. Sachdev and Peter J. Basser and Wei Wen and for the Alzheimer's Disease Neuroimaging Initiative}, doi = {10.1002/hbm.25628}, journal = {Human Brain Mapping}, pdf = {https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.25628}, title = {Orientational changes of white matter fibers in Alzheimer's disease and amnestic mild cognitive impairment}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.25628}, year = {2021} }Abstract
Abstract White matter abnormalities represent early neuropathological events in neurodegenerative diseases such as Alzheimer's disease (AD), investigating these white matter alterations would likely provide valuable insights into pathological changes over the course of AD. Using a novel mathematical framework called “Director Field Analysis” (DFA), we investigated the geometric microstructural properties (i.e., splay, bend, twist, and total distortion) in the orientation of white matter fibers in AD, amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) individuals from the Alzheimer's Disease Neuroimaging Initiative 2 database. Results revealed that AD patients had extensive orientational changes in the bilateral anterior thalamic radiation, corticospinal tract, inferior and superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and uncinate fasciculus in comparison with CN. We postulate that these orientational changes of white matter fibers may be partially caused by the expansion of lateral ventricle, white matter atrophy, and gray matter atrophy in AD. In contrast, aMCI individuals showed subtle orientational changes in the left inferior longitudinal fasciculus and right uncinate fasciculus, which showed a significant association with the cognitive performance, suggesting that these regions may be preferential vulnerable to breakdown by neurodegenerative brain disorders, thereby resulting in the patients' cognitive impairment. To our knowledge, this article is the first to examine geometric microstructural changes in the orientation of white matter fibers in AD and aMCI. Our findings demonstrate that the orientational information of white matter fibers could provide novel insight into the underlying biological and pathological changes in AD and aMCI.
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Hao Guan, Chaoyue Wang, Jian Cheng, Jing Jing, Tao Liu*,
"A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer's disease",
Human Brain Mapping (HBM), 2021.
[bibtex]
[abstract]
[citations: 22]
Bibtex
@article{guan:HBM2021, author = {Hao Guan and Chaoyue Wang and Jian Cheng and Jing Jing and Tao Liu}, doi = {10.1002/hbm.25685}, journal = {Human Brain Mapping}, pdf = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.25685}, publisher = {Wiley Online Library}, title = {A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer's disease}, url = {https://dx.doi.org/10.1002/hbm.25685}, year = {2021} }Abstract
Structural magnetic resonance imaging (sMRI) can capture the spatial patterns of brain atrophy in Alzheimer's disease (AD) and incipient dementia. Recently, many sMRI-based deep learning methods have been developed for AD diagnosis. Some of these methods utilize neural networks to extract high-level representations on the basis of handcrafted features, while others attempt to learn useful features from brain regions proposed by a separate module. However, these methods require considerable manual engineering. Their stepwise training procedures would introduce cascading errors. Here, we propose the parallel attention-augmented bilinear network, a novel deep learning framework for AD diagnosis. Based on a 3D convolutional neural network, the framework directly learns both global and local features from sMRI scans without any prior knowledge. The framework is lightweight and suitable for end-to-end training. We evaluate the framework on two public datasets (ADNI-1 and ADNI-2) containing 1,340 subjects. On both the AD classification and mild cognitive impairment conversion prediction tasks, our framework achieves competitive results. Furthermore, we generate heat maps that highlight discriminative areas for visual interpretation. Experiments demonstrate the effectiveness of the proposed framework when medical priors are unavailable or the computing resources are limited. The proposed framework is general for 3D medical image analysis with both efficiency and interpretability.
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Xinxin Li, Yu Zhao, Jiyang Jiang, Jian Cheng, Wanlin Zhu, Zhenzhou Wu, Jing Jing, Zhe Zhang, Wei Wen, Perminder S. Sachdev, Yongjun Wang, Tao Liu*, Zixiao Li*,
"White matter hyperintensities segmentation using an ensemble of neural networks",
Human Brain Mapping (HBM), 2021.
[bibtex]
[abstract]
[citations: 32]
Bibtex
@article{li:HBM2021, author = {Xinxin Li and Yu Zhao and Jiyang Jiang and Jian Cheng and Wanlin Zhu and Zhenzhou Wu and Jing Jing and Zhe Zhang and Wei Wen and Perminder S. Sachdev and Yongjun Wang and Tao Liu and Zixiao Li}, doi = {10.1002/hbm.25695}, journal = {Human Brain Mapping}, pdf = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.25695}, title = {White matter hyperintensities segmentation using an ensemble of neural networks}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.25695}, year = {2021} }Abstract
Abstract White matter hyperintensities (WMHs) represent the most common neuroimaging marker of cerebral small vessel disease (CSVD). The volume and location of WMHs are important clinical measures. We present a pipeline using deep fully convolutional network and ensemble models, combining U-Net, SE-Net, and multi-scale features, to automatically segment WMHs and estimate their volumes and locations. We evaluated our method in two datasets: a clinical routine dataset comprising 60 patients (selected from Chinese National Stroke Registry, CNSR) and a research dataset composed of 60 patients (selected from MICCAI WMH Challenge, MWC). The performance of our pipeline was compared with four freely available methods: LGA, LPA, UBO detector, and U-Net, in terms of a variety of metrics. Additionally, to access the model generalization ability, another research dataset comprising 40 patients (from Older Australian Twins Study and Sydney Memory and Aging Study, OSM), was selected and tested. The pipeline achieved the best performance in both research dataset and the clinical routine dataset with DSC being significantly higher than other methods (p < .001), reaching .833 and .783, respectively. The results of model generalization ability showed that the model trained on the research dataset (DSC = 0.736) performed higher than that trained on the clinical dataset (DSC = 0.622). Our method outperformed widely used pipelines in WMHs segmentation. This system could generate both image and text outputs for whole brain, lobar and anatomical automatic labeling WMHs. Additionally, software and models of our method are made publicly available at https://www.nitrc.org/projects/what\_v1.
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Haichao Zhao, Jian Cheng, Jiyang Jiang, Lijun Zuo, Wanlin Zhu, Wei Wen, Perminder Sachdev, Yongjun Wang, Tao Liu*, Zixiao Li*,
"Geometric microstructural damage of white matter with functional compensation in post-stroke",
Neuropsychologia, pp. 107980, 2021.
[bibtex]
[abstract]
[citations: 13]
Bibtex
@article{zhao:DFA:2021, author = {Haichao Zhao and Jian Cheng and Jiyang Jiang and Lijun Zuo and Wanlin Zhu and Wei Wen and Perminder Sachdev and Yongjun Wang and Tao Liu and Zixiao Li}, doi = {j.neuropsychologia.2021.107980}, journal = {Neuropsychologia}, pages = {107980}, publisher = {Elsevier}, title = {Geometric microstructural damage of white matter with functional compensation in post-stroke}, url = {https://dx.doi.org/j.neuropsychologia.2021.107980}, year = {2021} }Abstract
Background and purpose Subcortical ischemic stroke usually leads to the geometric microstructural changes in the orientation of peri- infarct white matter fiber. We conducted the study to determine the microstructural changes in the white matter fiber orientation in post stroke patients with and without cognitive impairment (PSCI, NPSCI), and to investigate the impact of peri-infarct white matter damage on the morphology and functional connectivity of their projective cerebral regions. Methods A novel mathematical framework called Director Field Analysis (DFA) was applied to study the microstructural changes in the orientation of white matter fiber in PSCI (n = 23), NPSCI (n = 17), and cognitively normal (CN, n = 29) individuals. Results PSCI patients had extensive abnormalities in the orientation of white matter fiber in the corpus callosum, bilateral internal capsule, external capsule, forceps major, forceps minor, and corticospinal tract in comparison with NPSCI and CN. NPSCI patients also showed significant increases in bend and twist of white matter fiber orientation in the internal capsule in comparison with CN. Seed-based functional connectivity analysis showed that peri-infarct white matter deficits indicate a significant impact on functional connectivity with related cortical regions, suggesting the coexistence of impairment and compensation in post-stroke. In addition, these peri-infarct white matter damages and abnormal functional connectivity were significantly correlated with cognitive scores. Machine learning model also indicated that these changes in white matter fiber orientation and functional connectivity can predict the cognitive status in post-stroke. Conclusions Post-stroke patients experienced pathological damage in the orientation of peri-infarct white matter fiber. The peri-infarct white matter damage may further induce the abnormal functional connectivity in projective cerebral regions. These degenerations of peri-infarct white matter fiber and associated functional connectivity changes may mediate the cognitive impairment in post- stroke.
2020
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Hao Liu, Tao Liu*, Jiyang Jiang, Jian Cheng, Yan Liu, Daqing Li, Chao Dong, Haijun Niu, Shuyu Li, Jicong Zhang, Henry Brodaty, Perminder Sachdev, Wei Wen,
"Differential longitudinal changes in structural complexity and volumetric measures in community-dwelling older individuals",
Neurobiology of Aging, vol. 91, pp. 26–35, 2020.
[bibtex]
[abstract]
[citations: 17]
Bibtex
@article{liu:NoA2020, author = {Hao Liu and Tao Liu and Jiyang Jiang and Jian Cheng and Yan Liu and Daqing Li and Chao Dong and Haijun Niu and Shuyu Li and Jicong Zhang and Henry Brodaty and Perminder Sachdev and Wei Wen}, doi = {10.1016/j.neurobiolaging.2020.02.023}, journal = {Neurobiology of Aging}, pages = {26-35}, publisher = {Elsevier}, title = {Differential longitudinal changes in structural complexity and volumetric measures in community-dwelling older individuals}, url = {https://dx.doi.org/10.1016/j.neurobiolaging.2020.02.023}, volume = {91}, year = {2020} }Abstract
Fractal geometry provides a method of analyzing natural and especially biological morphologies. To investigate the relationship between the complexity measure, which is indexed as fractal dimensionality (FD), and the traditional Euclidean metrics, such as the volume and thickness, of the brain in older age, we analyzed 483 MRI scans of 161 community-dwelling, nondemented individuals aged 70–90 years at the baseline and their 2-year and 6-year follow-ups. We quantified changes in neuroimaging metrics in cortical lobes and subcortical structures and investigated the effects of age, sex, hemisphere, and education on FD. We also analyzed the mediating effects of these metrics for further investigation. FD showed significant age-related decline in all structures, and its trajectory was best modeled quadratically in the bilateral frontal, parietal, and occipital lobes, as well as in the bilateral caudate, putamen, hippocampus, amygdala, and accumbens. FD showed specific mediating effects on aging and cognitive decline in some regions. Our findings suggest that FD is reliable yet shows a different pattern of decline compared with volumetric measures.
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Qiongge Li#, Chao Dong#, Tao Liu*, Xiaodan Chen, Alistair Perry, Jiyang Jiang, Jian Cheng, Haijun Niu, Nicole A. Kochan, Henry Brodaty, Perminder S. Sachdev, Wei Wen,
"Longitudinal Changes in Whole-Brain Functional Connectivity Strength Patterns and the Relationship With the Global Cognitive Decline in Older Adults",
Frontiers in Aging Neuroscience, vol. 12, pp. 71, 2020.
[bibtex]
[abstract]
[citations: 29]
Bibtex
@article{li:fan2020, author = {Qiongge Li and Chao Dong and Tao Liu and Xiaodan Chen and Alistair Perry and Jiyang Jiang and Jian Cheng and Haijun Niu and Nicole A. Kochan and Henry Brodaty and Perminder S. Sachdev and Wei Wen}, doi = {10.3389/fnagi.2020.00071}, journal = {Frontiers in Aging Neuroscience}, pages = {71}, pdf = {https://www.frontiersin.org/articles/10.3389/fnagi.2020.00071/pdf}, publisher = {Frontiers}, title = {Longitudinal Changes in Whole-Brain Functional Connectivity Strength Patterns and the Relationship With the Global Cognitive Decline in Older Adults}, url = {https://dx.doi.org/10.3389/fnagi.2020.00071}, volume = {12}, year = {2020} }Abstract
Aging is associated with changes in brain functional patterns as well as cognition. The present research sought to investigate longitudinal changes in whole brain functional connectivity strength (FCS) and cognitive performance scores in very old cognitively unimpaired individuals. We studied 34 cognitively normal elderly individuals at both baseline and 4-year follow-up (baseline age = 78 ± 3.14 years) with resting-state functional magnetic resonance imaging (r-fMRI), structural MRI scans, and neuropsychological assessments conducted. Voxel-based whole brain FCS was calculated and we found that bilateral superior parietal and medial frontal regions showed decreased FCS, while the supplementary motor area (SMA) and insula showed increased FCS with age, along with a decrease in bilateral prefrontal cortical thickness. The changes of FCS in left precuneus were associated with an aging-related decline in global cognition. Taken together, our results suggest changes in FCS with aging with the precuneus as a hub and this may underlie changes in global cognition that accompany aging. These findings help better understand the normal aging mechanism.
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Jing Zhang, Zixiao Li, Xingxing Cao, Lijun Zuo, Wei Wen, Wanlin Zhu, Jiyang Jiang, Jian Cheng, Perminder Sachdev, Tao Liu*, Yongjun Wang*,
"Altered Prefrontal--Basal Ganglia Effective Connectivity in Patients With Poststroke Cognitive Impairment",
Frontiers in Neurology, vol. 11, 2020.
[bibtex]
[abstract]
[citations: 10]
Bibtex
@article{zhang:FN2020, author = {Jing Zhang and Zixiao Li and Xingxing Cao and Lijun Zuo and Wei Wen and Wanlin Zhu and Jiyang Jiang and Jian Cheng and Perminder Sachdev and Tao Liu and Yongjun Wang}, doi = {10.3389/fneur.2020.577482}, journal = {Frontiers in Neurology}, pdf = {https://www.frontiersin.org/articles/10.3389/fneur.2020.577482/pdf}, publisher = {Frontiers Media SA}, title = {Altered Prefrontal--Basal Ganglia Effective Connectivity in Patients With Poststroke Cognitive Impairment}, url = {https://dx.doi.org/10.3389/fneur.2020.577482}, volume = {11}, year = {2020} }Abstract
We investigated the association between poststroke cognitive impairment and a specific effective network connectivity in the prefrontal–basal ganglia circuit. The resting-state effective connectivity of this circuit was modeled by employing spectral dynamic causal modeling in 11 poststroke patients with cognitive impairment (PSCI), 8 poststroke patients without cognitive impairment (non-PSCI) at baseline and 3-month follow-up, and 28 healthy controls. Our results showed that different neuronal models of effective connectivity in the prefrontal–basal ganglia circuit were observed among healthy controls, non-PSCI, and PSCI patients. Additional connected paths (extra paths) appeared in the neuronal models of stroke patients compared with healthy controls. Moreover, changes were detected in the extra paths of non-PSCI between baseline and 3-month follow-up poststroke, indicating reorganization in the ipsilesional hemisphere and suggesting potential compensatory changes in the contralesional hemisphere. Furthermore, the connectivity strengths of the extra paths from the contralesional ventral anterior nucleus of thalamus to caudate correlated significantly with cognitive scores in non-PSCI and PSCI patients. These suggest that the neuronal model of effective connectivity of the prefrontal–basal ganglia circuit may be sensitive to stroke- induced cognitive decline, and it could be a biomarker for poststroke cognitive impairment 3 months poststroke. Importantly, contralesional brain regions may play an important role in functional compensation of cognitive decline.
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Ziyang Liu#, Jian Cheng#, Haogang Zhu, Jicong Zhang, Tao Liu,
"Brain Age Estimation from MRI Using a Two-Stage Cascade Network with Ranking Loss",
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'20), pp. 198–207, 2020.
[bibtex]
[abstract]
[code]
[citations: 7]
(Ziyang Liu and Jian Cheng contributed equally.)
Bibtex
@inproceedings{liu:MICCAI2020, author = {Ziyang Liu and Jian Cheng and Haogang Zhu and Jicong Zhang and Tao Liu}, booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'20)}, doi = {10.1007/978-3-030-59728-3_20}, organization = {Springer}, pages = {198-207}, title = {Brain Age Estimation from MRI Using a Two-Stage Cascade Network with Ranking Loss}, url = {https://dx.doi.org/10.1007/978-3-030-59728-3_20}, year = {2020} }Abstract
As age increases, human brains will be aged, and people tend to experience cognitive decline with a higher risk of neuro- degenerative disease and dementia. Recently, it was reported that deep neural networks, e.g., 3D convolutional neural networks (CNN), are able to predict chronological age accurately in healthy people from their T1-weighted magnetic resonance images (MRI). The predicted age, called as “brain age” or “brain predicted age”, could be a biomarker of the brain ageing process. In this paper, we propose a novel 3D convolutional network, called as two-stage-age- net (TSAN), for brain age estimation from T1-weighted MRI data. Compared with the state-of-the-art CNN by Cole et al., TSAN has several improvements: 1) TSAN uses a two-stage cascade architecture, where the first network is to estimate a discretized age range, then the second network is to further estimate the brain age more accurately; 2) Besides using the traditional mean square error (MSE) loss between chronological and estimated ages, TSAN considers two additional novel ranking losses, based on paired samples and a batch of samples, for regularizing the training process; 3) TSAN uses densely connected paths to combine feature maps with different scales; 4) TSAN considers gender labels as input features for the network, considering brains of male and female age differently. The proposed TSAN was validated in three public datasets. The experiments showed that TSAN could provide accurate brain age estimation in healthy subjects, yielding a mean absolute error (MAE) of 2.428, and a Pearson’s correlation coefficient (PCC) of 0.985, between the estimated and the chronological ages.
2018
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Jian Cheng*, Dinggang Shen, Pew-Thian Yap*, Peter J. Basser*,
"Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes",
IEEE Transactions on Medical Imaging (TMI), vol. 37, no. 1, pp. 185–199, 2018.
[bibtex]
[abstract]
[arxiv]
[project]
[citations: 38]
Bibtex
@article{cheng:TMI2017, author = {Jian Cheng and Dinggang Shen and Pew-Thian Yap and Peter J. Basser}, doi = {10.1109/TMI.2017.2756072}, journal = {IEEE Transactions on Medical Imaging}, number = {1}, pages = {185-199}, pdf = {https://arxiv.org/pdf/1706.06682.pdf}, publisher = {IEEE}, title = {Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes}, url = {https://dx.doi.org/10.1109/TMI.2017.2756072}, volume = {37}, year = {2018} }Abstract
In diffusion MRI (dMRI), a good sampling scheme is important for efficient acquisition and robust reconstruction. Diffusion weighted signal is normally acquired on single or multiple shells in q-space. Signal samples are typically distributed uniformly on different shells to make them invariant to the orientation of structures within tissue, or the laboratory coordinate frame. The Electrostatic Energy Minimization (EEM) method, originally proposed for single shell sampling scheme in dMRI, was recently generalized to multi- shell schemes, called Generalized EEM (GEEM). GEEM has been successfully used in the Human Connectome Project (HCP). However, EEM does not directly address the goal of optimal sampling, i.e., achieving large angular separation between sampling points. In this paper, we propose a more natural formulation, called Spherical Code (SC), to directly maximize the minimal angle between different samples in single or multiple shells. We consider not only continuous problems to design single or multiple shell sampling schemes, but also discrete problems to uniformly extract sub-sampled schemes from an existing single or multiple shell scheme, and to order samples in an existing scheme. We propose five algorithms to solve the above problems, including an incremental SC (ISC), a sophisticated greedy algorithm called Iterative Maximum Overlap Construction (IMOC), an 1-Opt greedy method, a Mixed Integer Linear Programming (MILP) method, and a Constrained Non-Linear Optimization (CNLO) method. To our knowledge, this is the first work to use the SC formulation for single or multiple shell sampling schemes in dMRI. Experimental results indicate that SC methods obtain larger angular separation and better rotational invariance than the state- of-the-art EEM and GEEM. The related codes and a tutorial have been released in DMRITool.
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Jian Cheng*, Peter J. Basser*,
"Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in diffusion MRI",
Medical Image Analysis (MedIA), vol. 43, pp. 112–128, 2018.
[bibtex]
[abstract]
[arxiv]
[citations: 14]
Bibtex
@article{cheng:MedIA2017, author = {Jian Cheng and Peter J. Basser}, doi = {10.1016/j.media.2017.10.003}, journal = {Medical Image Analysis}, pages = {112-128}, pdf = {https://arxiv.org/pdf/1706.01862.pdf}, publisher = {Elsevier}, title = {Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in diffusion MRI}, url = {https://dx.doi.org/10.1016/j.media.2017.10.003}, volume = {43}, year = {2018} }Abstract
In Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI), a tensor field or a spherical function field (e.g., an orientation distribution function field), can be estimated from measured diffusion weighted images. In this paper, inspired by the microscopic theoretical treatment of phases in liquid crystals, we introduce a novel mathematical framework, called Director Field Analysis (DFA), to study local geometric structural information of white matter based on the reconstructed tensor field or spherical function field: 1) We propose a set of mathematical tools to process general director data, which consists of dyadic tensors that have orientations but no direction. 2) We propose Orientational Order (OO) and Orientational Dispersion (OD) indices to describe the degree of alignment and dispersion of a spherical function in a single voxel or in a region, respectively; 3) We also show how to construct a local orthogonal coordinate frame in each voxel exhibiting anisotropic diffusion; 4) Finally, we define three indices to describe three types of orientational distortion (splay, bend, and twist) in a local spatial neighborhood, and a total distortion index to describe distortions of all three types. To our knowledge, this is the first work to quantitatively describe orientational distortion (splay, bend, and twist) in general spherical function fields from DTI or HARDI data. The proposed DFA and its related mathematical tools can be used to process not only diffusion MRI data but also general director field data, and the proposed scalar indices are useful for detecting local geometric changes of white matter for voxel-based or tract-based analysis in both DTI and HARDI acquisitions. The related codes and a tutorial for DFA will be released in DMRITool.
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Yongqin Zhang, Feng Shi, Jian Cheng, Li Wang, Pew-Thian Yap, Dinggang Shen,
"Longitudinally Guided Super-Resolution of Neonatal Brain Magnetic Resonance Images",
IEEE Transactions on Cybernetics (TCYB), 2018.
[bibtex]
[abstract]
[citations: 41]
Bibtex
@article{zhang:TCYB2018, author = {Yongqin Zhang and Feng Shi and Jian Cheng and Li Wang and Pew-Thian Yap and Dinggang Shen}, doi = {10.1109/TCYB.2017.2786161}, journal = {IEEE Transactions on Cybernetics}, pdf = {https://www.researchgate.net/profile/Yongqin_Zhang2/publication/322348018_Longitudinally_Guided_Super-Resolution_of_Neonatal_Brain_Magnetic_Resonance_Images/links/5a846d6b4585159152b7fffe/Longitudinally-Guided-Super-Resolution-of-Neonatal-Brain-Magnetic-Resonance-Images.pdf}, title = {Longitudinally Guided Super-Resolution of Neonatal Brain Magnetic Resonance Images}, url = {https://dx.doi.org/10.1109/TCYB.2017.2786161}, year = {2018} }Abstract
Neonatal magnetic resonance (MR) images typically have low spatial resolution and insufficient tissue contrast. Interpolation methods are commonly used to upsample the images for the subsequent analysis. However, the resulting images are often blurry and susceptible to partial volume effects. In this paper, we propose a novel longitudinally guided super-resolution algorithm for neonatal images. This is motivated by the fact that anatomical structures evolve slowly and smoothly as the brain develops after birth. We propose a strategy involving longitudinal regularization, similar to bilateral filtering, in combination with lowrank and total variation constraints to solve the ill-posed inverse problem associated with image super-resolution. Experimental results on neonatal MR images demonstrate that the proposed algorithm recovers clear structural details and outperforms stateof-the-art methods both qualitatively and quantitatively.
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Jian Cheng, Tao Liu, Feng Shi, Ruiliang Bai, Jicong Zhang, Haogang Zhu, Dacheng Tao, Peter J Basser,
"On Quantifying Local Geometric Structures of Fiber Tracts",
Medical Image Computing and Computer-Assisted Intervention (MICCAI'18), pp. 392–400, 2018.
[bibtex]
[abstract]
[citations: 2]
Bibtex
@inproceedings{cheng:MICCAI2018, author = {Jian Cheng and Tao Liu and Feng Shi and Ruiliang Bai and Jicong Zhang and Haogang Zhu and Dacheng Tao and Peter J Basser}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI'18)}, doi = {10.1007/978-3-030-00931-1_45}, hal_id = {hal-02098668}, organization = {Springer}, pages = {392-400}, pdf = {https://hal.archives-ouvertes.fr/hal-02098668/document}, title = {On Quantifying Local Geometric Structures of Fiber Tracts}, url = {https://dx.doi.org/10.1007/978-3-030-00931-1_45}, year = {2018} }Abstract
In diffusion MRI, fiber tracts, represented by densely distributed 3D curves, can be estimated from diffusion weighted images using tractography. The spatial geometric structure of white matter fiber tracts is known to be complex in human brain, but it carries intrinsic information of human brain. In this paper, inspired by studies of liquid crystals, we propose tract-based director field analysis (tDFA) with total six rotationally invariant scalar indices to quantify local geometric structures of fiber tracts. The contributions of tDFA include: (1) We propose orientational order (OO) and orientational dispersion (OD) indices to quantify the degree of alignment and dispersion of fiber tracts; (2) We define the local orthogonal frame for a set of unoriented curves, which is proved to be a generalization of the Frenet frame defined for a single oriented curve; (3) With the local orthogonal frame, we propose splay, bend, and twist indices to quantify three types of orientational distortion of local fiber tracts, and a total distortion index to describe distortions of all three types. The proposed tDFA for fiber tracts is a generalization of the voxel- based DFA (vDFA) which was recently proposed for a spherical function field (i.e., an ODF field). To our knowledge, this is the first work to quantify orientational distortion (splay, bend, twist, and total distortion) of fiber tracts. Experiments show that the proposed scalar indices are useful descriptors of local geometric structures to visualize and analyze fiber tracts.
2017
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Hao Guan, Tao Liu*, Jiyang Jiang, Dacheng Tao, Jicong Zhang, Haijun Niu, Wanlin Zhu, Yilong Wang, Jian Cheng*, Nicole A. Kochan, Henry Brodaty, Perminder Sachdev, Wei Wen,
"Classifying MCI subtypes in community-dwelling elderly using cross-sectional and longitudinal MRI-based biomarkers",
Frontiers in Aging Neuroscience, vol. 9, pp. 309, 2017.
[bibtex]
[abstract]
[citations: 27]
Bibtex
@article{tao:cheng:FNAGI2017, author = {Hao Guan and Tao Liu and Jiyang Jiang and Dacheng Tao and Jicong Zhang and Haijun Niu and Wanlin Zhu and Yilong Wang and Jian Cheng and Nicole A. Kochan and Henry Brodaty and Perminder Sachdev and Wei Wen}, doi = {10.3389/fnagi.2017.00309}, journal = {Frontiers in Aging Neuroscience}, pages = {309}, pdf = {http://journal.frontiersin.org/article/10.3389/fnagi.2017.00309/pdf}, title = {Classifying MCI subtypes in community-dwelling elderly using cross-sectional and longitudinal MRI-based biomarkers}, url = {https://dx.doi.org/10.3389/fnagi.2017.00309}, volume = {9}, year = {2017} }Abstract
Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73–85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment.
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Jian Cheng, Peter J. Basser,
"Director Field Analysis to Explore Local White Matter Geometric Structure in diffusion MRI",
International Conference on Information Processing in Medical Imaging (IPMI'17), vol. 10265, pp. 427–439, 2017.
[bibtex]
[abstract]
(Oral presentation; Turned to be a poster due to conflicting travel plans.)
Bibtex
@inproceedings{cheng:IPMI2017, author = {Jian Cheng and Peter J. Basser}, booktitle = {International Conference on Information Processing in Medical Imaging (IPMI'17)}, doi = {10.1007/978-3-319-59050-9_34}, hal_id = {hal-01511573}, pages = {427-439}, pdf = {https://hal.archives-ouvertes.fr/hal-01511573/document}, title = {Director Field Analysis to Explore Local White Matter Geometric Structure in diffusion MRI}, url = {https://dx.doi.org/10.1007/978-3-319-59050-9_34}, volume = {10265}, year = {2017} }Abstract
In diffusion MRI, a tensor field or a spherical function field, e.g., an Orientation Distribution Function (ODF) field, are estimated from measured diffusion weighted images. In this paper, inspired by microscopic theoretical treatment of phases in liquid crystals, we introduce a novel mathematical framework, called Director Field Analysis (DFA), to study local geometric structural information of white matter from the estimated tensor field or spherical function field. 1) We propose Orientational Order (OO) and Orientational Dispersion (OD) indices to describe the degree of alignment and dispersion of a spherical function in each voxel; 2) We estimate a local orthogonal coordinate frame in each voxel with anisotropic diffusion; 3) Finally, we define three indices to describe three types of orientational distortion (splay, bend, and twist) in a local spatial neighborhood, and a total distortion index to describe distortions of all three types. To our knowledge, this is the first work to \emph{quantitatively} describe orientational distortion (splay, bend, and twist) in diffusion MRI. The proposed scalar indices are useful to detect local geometric changes of white matter for voxel-based or tract-based analysis in both DTI and HARDI acquisitions. -
Jian Cheng, Peter J. Basser,
"Exploring Local White Matter Geometric Structure in diffusion MRI Using Director Field Analysis",
Scientific Meeting and Exhibition of the ISMRM (ISMRM'17), 2017.
[bibtex]
Bibtex
@conference{cheng:ISMRM2017, author = {Jian Cheng and Peter J. Basser}, booktitle = {Scientific Meeting and Exhibition of the ISMRM (ISMRM'17)}, hal_id = {hal-01511564}, pdf = {https://hal.archives-ouvertes.fr/hal-01511564/document}, title = {Exploring Local White Matter Geometric Structure in diffusion MRI Using Director Field Analysis}, url = {http://submissions.mirasmart.com/ISMRM2017/ViewSubmissionPublic.aspx?sei=YLBxyBRee}, year = {2017} }
2016
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Jian Cheng, Hongtu Zhu,
"Diffusion Magnetic Resonance Imaging (dMRI)",
Handbook of Neuroimaging Data Analysis, 2016.
[bibtex]
[abstract]
[citations: 4]
Bibtex
@inbook{cheng:handbook2016, author = {Jian Cheng and Hongtu Zhu}, booktitle = {Handbook of Neuroimaging Data Analysis}, hal_id = {hal-01430475}, pdf = {https://hal.archives-ouvertes.fr/hal-01430475/document}, title = {Diffusion Magnetic Resonance Imaging (dMRI)}, url = {https://hal.archives-ouvertes.fr/hal-01430475}, year = {2016} }Abstract
Since the 1980s, diffusion magnetic resonance imaging (dMRI) as a magnetic resonance imaging (MRI) technique has been widely used to track the effective diffusion of water molecules, which is hindered by many obstacles (e.g., fibers or membranes), in the human brain in vivo. Because water molecules tend to diffuse slowly across white matter fibers and diffuse fast along such fibers, the use of dMRI to track water diffusion allows one to map the microstructure and organization of those white matter pathways (17). Quantitatively measuring the diffusion process is critical for a quantitative assessment of the integrity of anatomical connectivity in white matter and its association with brain functional connectivity. Its clinical applications include normal brain maturation and aging, cerebral ischemia, multiple sclerosis, epilepsy, metabolic disorders, and brain tumors, among many others. Although there are several nice review papers and monographies on dMRI (15, 5, 101, 81), this chapter was written for the readers who are interested in the theoretical underpinning of various mathematical and statistical methods associated with dMRI. Due to limitations of space, we are unable to cite all important papers in the dMRI literature.
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Ruiliang Bai, Dan Benjamini, Jian Cheng, Peter J. Basser,
"Fast, accurate 2D-MR relaxation exchange spectroscopy (REXSY): Beyond compressed sensing",
The Journal of Chemical Physics, vol. 145, no. 15, pp. 154202, 2016.
[bibtex]
[abstract]
[citations: 24]
Bibtex
@article{bai_JCP2016, author = {Ruiliang Bai and Dan Benjamini and Jian Cheng and Peter J. Basser}, doi = {10.1063/1.4964144}, journal = {The Journal of Chemical Physics}, number = {15}, pages = {154202}, pdf = {https://www.researchgate.net/profile/Ruiliang_Bai2/publication/309463759_14964144/links/5811be3008ae009606be8db0.pdf}, publisher = {AIP Publishing}, title = {Fast, accurate 2D-MR relaxation exchange spectroscopy (REXSY): Beyond compressed sensing}, url = {https://dx.doi.org/10.1063/1.4964144}, volume = {145}, year = {2016} }Abstract
Previously, we showed that compressive or compressed sensing (CS) can be used to reduce significantly the data required to obtain 2D-NMR relaxation and diffusion spectra when they are sparse or well localized. In some cases, an order of magnitude fewer uniformly sampled data were required to reconstruct 2D-MR spectra of comparable quality. Nonetheless, this acceleration may still not be sufficient to make 2D-MR spectroscopy practicable for many important applications, such as studying time-varying exchange processes in swelling gels or drying paints, in living tissue in response to various biological or biochemical challenges, and particularly for in vivo MRI applications. A recently introduced framework, marginal distributions constrained optimization (MADCO), tremendously accelerates such 2D acquisitions by using a priori obtained 1D marginal distribution as powerful constraints when 2D spectra are reconstructed. Here we exploit one important intrinsic property of the 2D-MR relaxation exchange spectra: the fact that the 1D marginal distributions of each 2D-MR relaxation exchange spectrum in both dimensions are equal and can be rapidly estimated from a single Carr-Purcell-Meiboom-Gill (CPMG) or inversion recovery prepared CPMG measurement. We extend the MADCO framework by further proposing to use the 1D marginal distributions to inform the subsequent 2D data- sampling scheme, concentrating measurements where spectral peaks are present and reducing them where they are not. In this way we achieve compression or acceleration that is an order of magnitude greater than that in our previous CS method while providing data in reconstructed 2D-MR spectral maps of comparable quality, demonstrated using several simulated and real 2D T2-T2 experimental data. This method, which can be called "informed compressed sensing", is extendable to other 2D- and even ND-MR exchange spectroscopy.
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Shangbang Rao, Joseph G. Ibrahim, Jian Cheng, Pew-Thian Yap, Hongtu Zhu*,
"SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging",
Journal of Computational and Graphical Statistics, vol. 25, no. 4, pp. 1195–1211, 2016.
[bibtex]
[abstract]
[citations: 7]
Bibtex
@article{rao_2015, author = {Shangbang Rao and Joseph G. Ibrahim and Jian Cheng and Pew-Thian Yap and Hongtu Zhu}, doi = {10.1080/10618600.2015.1105750}, journal = {Journal of Computational and Graphical Statistics}, number = {4}, pages = {1195-1211}, pdf = {https://www.researchgate.net/profile/Jian_Cheng2/publication/283828288_SR-HARDI_Spatially_Regularizing_High_Angular_Resolution_Diffusion_Imaging/links/584bc62408aecb6bd8c285a1.pdf}, title = {SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging}, url = {https://dx.doi.org/10.1080/10618600.2015.1105750}, volume = {25}, year = {2016} }Abstract
High angular resolution diffusion imaging (HARDI) has recently been of great interest in mapping the orientation of intravoxel crossing fibers, and such orientation information allows one to infer the connectivity patterns prevalent among different brain regions and possible changes in such connectivity over time for various neurodegenerative and neuropsychiatric diseases. The aim of this article is to propose a penalized multiscale adaptive regression model (PMARM) framework to spatially and adaptively infer the orientation distribution function (ODF) of water diffusion in regions with complex fiber configurations. In PMARM, we reformulate the HARDI imaging reconstruction as a weighted regularized least- square regression (WRLSR) problem. Similarity and distance weights are introduced to account for spatial smoothness of HARDI, while preserving the unknown discontinuities (e.g., edges between white matter and gray matter) of HARDI. The L1 penalty function is introduced to ensure the sparse solutions of ODFs, while a scaled L1 weighted estimator is calculated to correct the bias introduced by the L1 penalty at each voxel. In PMARM, we integrate the multiscale adaptive regression models, the propagation-separation method, and Lasso (least absolute shrinkage and selection operator) to adaptively estimate ODFs across voxels. Experimental results indicate that PMARM can reduce the angle detection errors on fiber crossing area and provide more accurate reconstruction than standard voxel-wise methods. Supplementary materials for this article are available online.
2015
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Feng Shi#, Jian Cheng#, Li Wang, Pew-Thian Yap, Dinggang Shen*,
"LRTV: MR image super-resolution with low-rank and total variation regularizations",
IEEE Transactions on Medical Imaging (TMI), vol. 34, no. 12, pp. 2459–2466, 2015.
[bibtex]
[abstract]
[code]
[citations: 337]
(F. Shi and J. Cheng contributed equally.)
Bibtex
@article{shi_TMI2015, author = {Feng Shi and Jian Cheng and Li Wang and Pew-Thian Yap and Dinggang Shen}, doi = {10.1109/TMI.2015.2437894}, hal_id = {hal-01154770}, journal = {IEEE Transactions on Medical Imaging}, number = {12}, pages = {2459-2466}, pdf = {https://hal.archives-ouvertes.fr/hal-01154770/document}, publisher = {IEEE}, title = {LRTV: MR image super-resolution with low-rank and total variation regularizations}, url = {https://dx.doi.org/10.1109/TMI.2015.2437894}, volume = {34}, year = {2015} }Abstract
Image super-resolution (SR) aims to recover high-resolution images from their low-resolution counterparts for improving image analysis and visualization. Interpolation methods, widely used for this purpose, often result in images with blurred edges and blocking effects. More advanced methods such as total variation (TV) retain edge sharpness during image recovery. However, these methods only utilize information from local neighborhoods, neglecting useful information from remote voxels. In this paper, we propose a novel image SR method that integrates both local and global information for effective image recovery. This is achieved by, in addition to TV, low-rank regularization that enables utilization of information throughout the image. The optimization problem can be solved effectively via alternating direction method of multipliers (ADMM). Experiments on MR images of both adult and pediatric subjects demonstrate that the proposed method enhances the details in the recovered high-resolution images, and outperforms methods such as the nearest-neighbor interpolation, cubic interpolation, iterative back projection (IBP), non-local means (NLM), and TV-based up- sampling.
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Jian Cheng, Dinggang Shen, Pew-Thian Yap, Peter J. Basser,
"Tensorial Spherical Polar Fourier diffusion MRI with Optimal Dictionary Learning",
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'15), vol. 9349, pp. 174–182, 2015.
[bibtex]
[abstract]
[project]
[citations: 13]
Bibtex
@inproceedings{cheng_MICCAI2015, author = {Jian Cheng and Dinggang Shen and Pew-Thian Yap and Peter J. Basser}, booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'15)}, doi = {10.1007/978-3-319-24553-9_22}, organization = {Springer}, pages = {174-182}, pdf = {https://hal.archives-ouvertes.fr/hal-01164809/document}, series = {LNCS}, title = {Tensorial Spherical Polar Fourier diffusion MRI with Optimal Dictionary Learning}, url = {https://dx.doi.org/10.1007/978-3-319-24553-9_22}, volume = {9349}, year = {2015} }Abstract
High Angular Resolution Diffusion Imaging (HARDI) can characterize complex white matter micro-structure, avoiding the Gaussian diffusion assumption inherent in Diffusion Tensor Imaging (DTI). However, HARDI methods normally require significantly more signal measurements and a longer scan time than DTI, which limits its clinical utility. By considering sparsity of the diffusion signal, Compressed Sensing (CS) allows robust signal reconstruction from relatively fewer samples, reducing the scanning time. A good dictionary that sparsifies the signal is crucial for CS reconstruction. In this paper, we propose a novel method called Tensorial Spherical Polar Fourier Imaging (TSPFI) to recover continuous diffusion signal and diffusion propagator by representing the diffusion signal using an orthonormal TSPF basis. TSPFI is a generalization of the existing model-based method DTI and the model- free method SPFI. We also propose dictionary learning TSPFI (DL- TSPFI) to learn an even sparser dictionary represented as a linear combination of TSPF basis from continuous mixture of Gaussian signals. The learning process is efficiently performed in a small subspace of SPF coefficients, and the learned dictionary is proved to be sparse for all mixture of Gaussian signals by adaptively setting the tensor in TSPF basis. Then the learned DL-TSPF dictionary is optimally and adaptively applied to different voxels using DTI and a weighted LASSO for CS reconstruction. DL-TSPFI is a generalization of DL-SPFI, by considering general adaptive tensor setting instead of a scale value. The experiments demonstrated that the learned DL-TSPF dictionary has a sparser representation and lower reconstruction Root-Mean-Squared-Error (RMSE) than both the original SPF basis and the DL-SPF dictionary.
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Jian Cheng, Dinggang Shen, Pew-Thian Yap, Peter J. Basser,
"Novel single and multiple shell uniform sampling schemes for diffusion MRI using spherical codes",
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'15), vol. 9349, pp. 28–36, 2015.
[bibtex]
[abstract]
[project]
[citations: 9]
(early accepted)
Bibtex
@inproceedings{cheng_MICCAI2015_sampling, author = {Jian Cheng and Dinggang Shen and Pew-Thian Yap and Peter J. Basser}, booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'15)}, doi = {10.1007/978-3-319-24553-9_4}, organization = {Springer}, pages = {28-36}, pdf = {https://hal.archives-ouvertes.fr/hal-01154774/document}, title = {Novel single and multiple shell uniform sampling schemes for diffusion MRI using spherical codes}, url = {https://dx.doi.org/10.1007/978-3-319-24553-9_4}, volume = {9349}, year = {2015} }Abstract
A good data sampling scheme is important for diffusion MRI acquisition and reconstruction. Diffusion Weighted Imaging (DWI) data is normally acquired on single or multiple shells in q-space. The samples in different shells are typically distributed uniformly, because they should be invariant to the orientation of structures within tissue, or the laboratory coordinate frame. The Electrostatic Energy Minimization (EEM) method, originally proposed for single shell sampling scheme in dMRI by Jones et al., was recently generalized to the multi-shell case, called generalized EEM (GEEM). GEEM has been successfully used in the Human Connectome Project (HCP). Recently, the Spherical Code (SC) concept was proposed to maximize the minimal angle between different samples in single or multiple shells, producing a larger angular separation and better rotational invariance than the GEEM method. In this paper, we propose two novel algorithms based on the SC concept: 1) an efficient incremental constructive method, called Iterative Maximum Overlap Construction (IMOC), to generate a sampling scheme on a discretized sphere; 2) a constrained non-linear optimization (CNLO) method to update a given initial scheme on the continuous sphere. Compared to existing incremental estimation methods, IMOC obtains schemes with much larger separation angles between samples, which are very close to the best known solutions in single shell case. Compared to the existing Riemannian gradient descent method, CNLO is more robust and stable. Experiments demonstrated that the two proposed methods provide larger separation angles and better rotational invariance than the state-of-the-art GEEM and methods based on the SC concept.
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Jian Cheng, Dinggang Shen, Peter J. Basser, Pew-Thian Yap,
"Joint 6D k-q Space Compressed Sensing for Accelerated High Angular Resolution Diffusion MRI",
International Conference on Information Processing in Medical Imaging (IPMI'15), vol. 9123, pp. 782–793, 2015.
[bibtex]
[abstract]
[citations: 32]
Bibtex
@inproceedings{cheng_IPMI2015, author = {Jian Cheng and Dinggang Shen and Peter J. Basser and Pew-Thian Yap}, booktitle = {International Conference on Information Processing in Medical Imaging (IPMI'15)}, doi = {10.1007/978-3-319-19992-4_62}, organization = {Springer}, pages = {782-793}, pdf = {https://hal.archives-ouvertes.fr/hal-01356133/document}, title = {Joint 6D k-q Space Compressed Sensing for Accelerated High Angular Resolution Diffusion MRI}, url = {https://dx.doi.org/10.1007/978-3-319-19992-4_62}, volume = {9123}, year = {2015} }Abstract
High Angular Resolution Diffusion Imaging (HARDI) avoids the Gaussian diffusion assumption that is inherent in Diffusion Tensor Imaging (DTI), and is capable of characterizing complex white matter micro-structure with greater precision. However, HARDI methods such as Diffusion Spectrum Imaging (DSI) typically require significantly more signal measurements than DTI, resulting in prohibitively long scanning times. One of the goals in HARDI research is therefore to improve estimation of quantities such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF) with a limited number of diffusion-weighted measurements. A popular approach to this problem, Compressed Sensing (CS), affords highly accurate signal reconstruction using significantly fewer (sub- Nyquist) data points than required traditionally. Existing approaches to CS diffusion MRI (CS-dMRI) mainly focus on applying CS in the q-space of diffusion signal measurements and fail to take into consideration information redundancy in the k-space. In this paper, we propose a framework, called 6-Dimensional Compressed Sensing diffusion MRI (6D-CS-dMRI), for reconstruction of the diffusion signal and the EAP from data sub-sampled in both 3D k-space and 3D q-space. To our knowledge, 6D-CS-dMRI is the first work that applies compressed sensing in the full 6D k-q space and reconstructs the diffusion signal in the full continuous q-space and the EAP in continuous displacement space. Experimental results on synthetic and real data demonstrate that, compared with full DSI sampling in k-q space, 6D-CS-dMRI yields excellent diffusion signal and EAP reconstruction with low root-mean-square error (RMSE) using 11 times less samples (3-fold reduction in k-space and 3.7-fold reduction in q-space).
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Feng Shi, Jian Cheng, Li Wang, Pew-Thian Yap, Dinggang Shen,
"Super-Resolution Reconstruction of Diffusion-Weighted Images Using 4D Low-Rank and Total Variation",
MICCAI workshop on Computational Diffusion MRI (CDMRI'15), pp. 15–25, 2015.
[bibtex]
[abstract]
[citations: 15]
(Oral presentation)
Bibtex
@inproceedings{shi:CDMRI2015, author = {Feng Shi and Jian Cheng and Li Wang and Pew-Thian Yap and Dinggang Shen}, booktitle = {MICCAI workshop on Computational Diffusion MRI (CDMRI'15)}, doi = {10.1007/978-3-319-28588-7_2}, pages = {15-25}, pdf = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5102512/pdf/nihms772323.pdf}, publisher = {Springer}, title = {Super-Resolution Reconstruction of Diffusion-Weighted Images Using 4D Low-Rank and Total Variation}, url = {https://dx.doi.org/10.1007/978-3-319-28588-7_2}, year = {2015} }Abstract
Diffusion-weighted imaging (DWI) provides invaluable information in white matter microstructure and is widely applied in neurological applications. However, DWI is largely limited by its relatively low spatial resolution. In this paper, we propose an image post- processing method, referred to as super-resolution reconstruction, to estimate a high spatial resolution DWI from the input low- resolution DWI, e.g., at a factor of 2. Instead of requiring specially designed DWI acquisition of multiple shifted or orthogonal scans, our method needs only a single DWI scan. To do that, we propose to model both the blurring and downsampling effects in the image degradation process where the low-resolution image is observed from the latent high-resolution image, and recover the latent high- resolution image with the help of two regularizations. The first regularization is four-dimensional (4D) low-rank, proposed to gather self-similarity information from both the spatial domain and the diffusion domain of 4D DWI. The second regularization is total variation, proposed to depress noise and preserve local structures such as edges in the image recovery process. Extensive experiments were performed on 20 subjects, and results show that the proposed method is able to recover the fine details of white matter structures, and outperform other approaches such as interpolation methods, non-local means based upsampling, and total variation based upsampling.
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Jian Cheng, Dinggang Shen, Pew-Thian Yap, Peter J. Basser,
"Novel Single and Multiple Shell Gradient Sampling Schemes for Diffusion MRI Using Spherical Codes",
23rd Scientific Meeting and Exhibition of the ISMRM (ISMRM'15), 2015.
[bibtex]
Bibtex
@conference{cheng:ISMRM2015, author = {Jian Cheng and Dinggang Shen and Pew-Thian Yap and Peter J. Basser}, booktitle = {23rd Scientific Meeting and Exhibition of the ISMRM (ISMRM'15)}, hal_id = {hal-01154773}, pdf = {https://hal.archives-ouvertes.fr/hal-01154773/document}, title = {Novel Single and Multiple Shell Gradient Sampling Schemes for Diffusion MRI Using Spherical Codes}, url = {https://hal.archives-ouvertes.fr/hal-01154773}, year = {2015} }
2014
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Jian Cheng*, Rachid Deriche, Tianzi Jiang, Dinggang Shen, Pew-Thian Yap*,
"Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI",
NeuroImage, vol. 101, pp. 750–764, 2014.
[bibtex]
[abstract]
[citations: 43]
(Honorable Mention in ISBI HARDI Reconstruction Challenge in 2013; the best technique in terms of local comparison.)
Bibtex
@article{cheng_NI2014, author = {Jian Cheng and Rachid Deriche and Tianzi Jiang and Dinggang Shen and Pew-Thian Yap}, doi = {10.1016/j.neuroimage.2014.07.062}, journal = {NeuroImage}, pages = {750-764}, pdf = {https://pdfs.semanticscholar.org/fc76/327ac26d573283db98939119660588c2af6f.pdf}, publisher = {Elsevier}, title = {Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI}, url = {https://dx.doi.org/10.1016/j.neuroimage.2014.07.062}, volume = {101}, year = {2014} }Abstract
Spherical Deconvolution (SD) is commonly used for estimating fiber Orientation Distribution Functions (fODFs) from diffusion-weighted signals. Existing SD methods can be classified into two categories: 1) Continuous Representation based SD (CR-SD), where typically Spherical Harmonic (SH) representation is used for convenient analytical solutions, and 2) Discrete Representation based SD (DR- SD), where the signal profile is represented by a discrete set of basis functions uniformly oriented on the unit sphere. A feasible fODF should be non-negative and should integrate to unity throughout the unit sphere S 2. However, to our knowledge, most existing SH- based SD methods enforce non-negativity only on discretized points and not the whole continuum of S 2. Maximum Entropy SD (MESD) and Cartesian Tensor Fiber Orientation Distributions (CT-FOD) are the only SD methods that ensure non-negativity throughout the unit sphere. They are however computational intensive and are susceptible to errors caused by numerical spherical integration. Existing SD methods are also known to overestimate the number of fiber directions, especially in regions with low anisotropy. DR-SD introduces additional error in peak detection owing to the angular discretization of the unit sphere. This paper proposes a SD framework, called Non-Negative SD (NNSD), to overcome all the limitations above. NNSD is significantly less susceptible to the false-positive peaks, uses SH representation for efficient analytical spherical deconvolution, and allows accurate peak detection throughout the whole unit sphere. We further show that NNSD and most existing SD methods can be extended to work on multi- shell data by introducing a three-dimensional fiber response function. We evaluated NNSD in comparison with Constrained SD (CSD), a quadratic programming variant of CSD, MESD, and an L1-norm regularized non-negative least-squares DR-SD. Experiments on synthetic and real single-/multi-shell data indicate that NNSD improves estimation performance in terms of mean difference of angles, peak detection consistency, and anisotropy contrast between isotropic and anisotropic regions.
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Rui Min, Guorong Wu, Jian Cheng, Qian Wang, Dinggang Shen*,
"Multi-atlas based representations for Alzheimer's disease diagnosis",
Human Brain Mapping (HBM), vol. 35, no. 10, pp. 5052–5070, 2014.
[bibtex]
[abstract]
[citations: 88]
Bibtex
@article{min_HBM2014, author = {Rui Min and Guorong Wu and Jian Cheng and Qian Wang and Dinggang Shen}, doi = {10.1002/hbm.22531}, journal = {Human Brain Mapping}, number = {10}, pages = {5052-5070}, pdf = {https://www.researchgate.net/profile/Jian_Cheng2/publication/261772139_Multi-Atlas_Based_Representations_for_Alzheimer%27s_Disease_Diagnosis/links/584bc35808aed95c24fbf813/Multi-Atlas-Based-Representations-for-Alzheimers-Disease-Diagnosis.pdf}, publisher = {Wiley Online Library}, title = {Multi-atlas based representations for Alzheimer's disease diagnosis}, url = {https://dx.doi.org/10.1002/hbm.22531}, volume = {35}, year = {2014} }Abstract
Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a pre-defined atlas) via non-linear registration, thus the corresponding regions in different brains can be compared. However, representations generated from one single atlas may not be sufficient to reveal the underlying anatomical differences between the groups of disease- affected patients and normal controls (NC). In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases. Representations generated from different atlases can thus provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors. Specifically, each studied subject is registered to multiple atlases, where adaptive regional features are extracted. Then, all features from different atlases are jointly selected by a correlation and relevance based scheme, followed by final classification with the support vector machine (SVM). We have evaluated the proposed method on 459 subjects (97 AD, 117 progressive-MCI (p-MCI), 117 stable-MCI (s-MCI), and 128 NC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieved 91.64% for AD/NC classification and 72.41% for p-MCI/s-MCI classification. Our results clearly demonstrate that the proposed multi-atlas based method can significantly outperform the previous single-atlas based methods.
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Jian Cheng, Dinggang Shen, Pew-Thian Yap,
"Designing single- and multiple-shell sampling schemes for diffusion MRI using spherical code",
Medical Image Computing and Computer-Assisted Intervention (MICCAI'14), vol. 8675, pp. 281–288, 2014.
[bibtex]
[abstract]
[project]
[citations: 23]
(early accepted)
Bibtex
@inproceedings{cheng_MICCAI2014, author = {Jian Cheng and Dinggang Shen and Pew-Thian Yap}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI'14)}, doi = {10.1007/978-3-319-10443-0_36}, hal_id = {hal-01011897}, pages = {281-288}, pdf = {https://hal.archives-ouvertes.fr/hal-01011897/document}, publisher = {Springer}, title = {Designing single- and multiple-shell sampling schemes for diffusion MRI using spherical code}, url = {https://dx.doi.org/10.1007/978-3-319-10443-0_36}, volume = {8675}, year = {2014} }Abstract
In diffusion MRI (dMRI), determining an appropriate sampling scheme is crucial for acquiring the maximal amount of information for data reconstruction and analysis using the minimal amount of time. For single-shell acquisition, uniform sampling without directional preference is usually favored. To achieve this, a commonly used approach is the Electrostatic Energy Minimization (EEM) method introduced in dMRI by Jones et al. However, the electrostatic energy formulation in EEM is not directly related to the goal of optimal sampling-scheme design, i.e., achieving large angular separation between sampling points. A mathematically more natural approach is to consider the Spherical Code (SC) formulation, which aims to achieve uniform sampling by maximizing the minimal angular difference between sampling points on the unit sphere. Although SC is well studied in the mathematical literature, its current formulation is limited to a single shell and is not applicable to multiple shells. Moreover, SC, or more precisely continuous SC (CSC), currently can only be applied on the continuous unit sphere and hence cannot be used in situations where one or several subsets of sampling points need to be determined from an existing sampling scheme. In this case, discrete SC (DSC) is required. In this paper, we propose novel DSC and CSC methods for designing uniform single-/multi-shell sampling schemes. The DSC and CSC formulations are solved respectively by Mixed Integer Linear Programming (MILP) and a gradient descent approach. A fast greedy incremental solution is also provided for both DSC and CSC. To our knowledge, this is the first work to use SC formulation for designing sampling schemes in dMRI. Experimental results indicate that our methods obtain larger angular separation and better rotational invariance than the generalized EEM (gEEM) method currently used in the Human Connectome Project (HCP).
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Rui Min, Jian Cheng, True Price, Guorong Wu, Dinggang Shen,
"Maximum-margin based representation learning from multiple atlases for Alzheimer's disease classification",
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'14), vol. 8674, pp. 212–219, 2014.
[bibtex]
[abstract]
[citations: 14]
Bibtex
@inproceedings{min:MICCAI2014, author = {Rui Min and Jian Cheng and True Price and Guorong Wu and Dinggang Shen}, booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'14)}, doi = {10.1007/978-3-319-10470-6_27}, organization = {Springer}, pages = {212-219}, pdf = {https://www.researchgate.net/profile/Jian_Cheng2/publication/269284266_Maximum-Margin_Based_Representation_Learning_from_Multiple_Atlases_for_Alzheimer's_Disease_Classification/links/584bc76608aed95c24fbf847.pdf}, title = {Maximum-margin based representation learning from multiple atlases for Alzheimer's disease classification}, url = {https://dx.doi.org/10.1007/978-3-319-10470-6_27}, volume = {8674}, year = {2014} }Abstract
In order to establish the correspondences between different brains for comparison, spatial normalization based morphometric measurements have been widely used in the analysis of Alzheimer's disease (AD). In the literature, different subjects are often compared in one atlas space, which may be insufficient in revealing complex brain changes. In this paper, instead of deploying one atlas for feature extraction and classification, we propose a maximum- margin based representation learning (MMRL) method to learn the optimal representation from multiple atlases. Unlike traditional methods that perform the representation learning separately from the classification, we propose to learn the new representation jointly with the classification model, which is more powerful in discriminating AD patients from normal controls (NC). We evaluated the proposed method on the ADNI database, and achieved 90.69% for AD/NC classification and 73.69% for p-MCI/s-MCI classification.
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Feng Shi#, Jian Cheng#, Li Wang, Pew-Thian Yap, Dinggang Shen,
"Longitudinal guided super-resolution reconstruction of neonatal brain MR images",
MICCAI Workshop on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, vol. 8682, pp. 67–76, 2014.
[bibtex]
[abstract]
[citations: 7]
(F. Shi and J. Cheng contributed equally.)
Bibtex
@inproceedings{shi:2014, author = {Feng Shi and Jian Cheng and Li Wang and Pew-Thian Yap and Dinggang Shen}, booktitle = {MICCAI Workshop on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data}, doi = {10.1007/978-3-319-14905-9_6}, organization = {Springer}, pages = {67-76}, pdf = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4669195/pdf/nihms691856.pdf}, title = {Longitudinal guided super-resolution reconstruction of neonatal brain MR images}, url = {https://dx.doi.org/10.1007/978-3-319-14905-9_6}, volume = {8682}, year = {2014} }Abstract
Neonatal images have low spatial resolution and insufficient tissue contrast. Generally, interpolation methods are used to upsample neonatal images to a higher resolution for more effective image analysis. However, the resulting images are often blurry and are susceptible to partial volume effect. In this paper, we propose an algorithm that utilizes longitudinal prior information for effective super-resolution reconstruction of neonatal images. We use a non- local approach to learn the spatial relationships of brain structures in high-resolution longitudinal images and apply this information to the super-resolution reconstruction of the neonatal image. In other words, the recurring patterns throughout the longitudinal scans are leveraged for reconstructing the neonatal image with high resolution. To solve this otherwise ill-posed inverse problem, low-rank and total-variation regularizations are enforced. Experiments performed on both T1- and T2-weighted MR images of 28 neonates demonstrate that the proposed method is capable of recovering more structural details and outperforms methods such as nearest neighbor interpolation, spline-based interpolation, non-local means upsampling, and both low-rank and total variation based super-resolution
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Jian Cheng, Dinggang Shen, Pew-Thian Yap,
"Joint k-q Space Compressed Sensing for Accelerated Multi-Shell Acquisition and Reconstruction of the diffusion signal and Ensemble Average Propagator",
ISMRM'14, 2014.
[bibtex]
(Oral presentation)
Bibtex
@conference{cheng_ISMRM2014, author = {Jian Cheng and Dinggang Shen and Pew-Thian Yap}, booktitle = {ISMRM'14}, hal_id = {hal-01011893}, pdf = {https://hal.archives-ouvertes.fr/hal-01011893/document}, title = {Joint k-q Space Compressed Sensing for Accelerated Multi-Shell Acquisition and Reconstruction of the diffusion signal and Ensemble Average Propagator}, url = {https://hal.archives-ouvertes.fr/hal-01011893}, year = {2014} } -
Jian Cheng, Pew-Thian Yap, Dinggang Shen,
"Single and Multiple Shell Sampling Design in dMRI Using Spherical code and Mixed Integer Linear Programming",
ISMRM'14, pp. 2558, May, 2014.
[bibtex]
Bibtex
@conference{cheng:ISMRM2014:sampling, address = {Italy}, author = {Jian Cheng and Pew-Thian Yap and Dinggang Shen}, booktitle = {ISMRM'14}, hal_id = {hal-01011892}, month = {May}, pages = {2558}, pdf = {https://hal.archives-ouvertes.fr/hal-01011892/document}, title = {Single and Multiple Shell Sampling Design in dMRI Using Spherical code and Mixed Integer Linear Programming}, url = {https://hal.archives-ouvertes.fr/hal-01011892}, year = {2014} }
2013
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Jian Cheng, Tianzi Jiang, Rachid Deriche, Dinggang Shen, Pew-Thian Yap,
"Regularized Spherical Polar Fourier Diffusion MRI with Optimal Dictionary Learning",
The 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'13), vol. 8149, pp. 639–646, 2013.
[bibtex]
[abstract]
[project]
[citations: 16]
Bibtex
@inproceedings{cheng_DL-SPFI_MICCAI13, author = {Jian Cheng and Tianzi Jiang and Rachid Deriche and Dinggang Shen and Pew-Thian Yap}, booktitle = {The 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'13)}, doi = {10.1007/978-3-642-40811-3_80}, hal_id = {hal-00824507}, pages = {639-646}, pdf = {https://hal.inria.fr/hal-00824507/document}, title = {Regularized Spherical Polar Fourier Diffusion MRI with Optimal Dictionary Learning}, url = {https://dx.doi.org/10.1007/978-3-642-40811-3_80}, volume = {8149}, year = {2013} }Abstract
Compressed Sensing (CS) takes advantage of signal sparsity or compressibility and allows superb signal reconstruction from relatively few measurements. Based on CS theory, a suitable dictionary for sparse representation of the signal is required. In diffusion MRI (dMRI), CS methods proposed for reconstruction of diffusion-weighted signal and the Ensemble Average Propagator (EAP) utilize two kinds of Dictionary Learning (DL) methods: 1) Discrete Representation DL (DR-DL), and 2) Continuous Representation DL (CR- DL). DR-DL is susceptible to numerical inaccuracy owing to interpolation and regridding errors in a discretized q-space. In this paper, we propose a novel CR-DL approach, called Dictionary Learning - Spherical Polar Fourier Imaging (DL-SPFI) for effective compressed-sensing reconstruction of the q-space diffusion-weighted signal and the EAP. In DL-SPFI, a dictionary that sparsifies the signal is learned from the space of continuous Gaussian diffusion signals. The learned dictionary is then adaptively applied to different voxels using a weighted LASSO framework for robust signal reconstruction. Compared with the start-of-the-art CR-DL and DR-DL methods proposed by Merlet et al. and Bilgic et al., respectively, our work offers the following advantages. First, the learned dictionary is proved to be optimal for Gaussian diffusion signals. Second, to our knowledge, this is the first work to learn a voxel- adaptive dictionary. The importance of the adaptive dictionary in EAP reconstruction will be demonstrated theoretically and empirically. Third, optimization in DL-SPFI is only performed in a small subspace resided by the SPF coefficients, as opposed to the q-space approach utilized by Merlet et al. We experimentally evaluated DL-SPFI with respect to L1-norm regularized SPFI (L1-SPFI), which uses the original SPF basis, and the DR-DL method proposed by Bilgic et al. The experiment results on synthetic and real data indicate that the learned dictionary produces sparser coefficients than the original SPF basis and results in significantly lower reconstruction error than Bilgic et al.'s method.
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Feng Shi#, Jian Cheng#, Li Wang, Pew-Thian Yap, Dinggang Shen,
"Low-Rank Total Variation for Image Super-Resolution",
Medical Image Computing and Computer-Assisted Intervention (MICCAI'13), vol. 8149, pp. 155–162, 2013.
[bibtex]
[abstract]
[code]
[citations: 54]
(F. Shi and J. Cheng contributed equally.)
Bibtex
@inproceedings{shi_MICCAI13, author = {Feng Shi and Jian Cheng and Li Wang and Pew-Thian Yap and Dinggang Shen}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI'13)}, doi = {10.1007/978-3-642-40811-3_20}, pages = {155-162}, pdf = {https://www.researchgate.net/profile/Jian_Cheng2/publication/260127836_Low-Rank_Total_Variation_for_Image_Super-Resolution/links/0a85e52f69f43abff6000000.pdf}, publisher = {Springer}, title = {Low-Rank Total Variation for Image Super-Resolution}, url = {https://dx.doi.org/10.1007/978-3-642-40811-3_20}, volume = {8149}, year = {2013} }Abstract
Most natural images can be approximated using their low-rank components. This fact has been successfully exploited in recent advancements of matrix completion algorithms for image recovery. However, a major limitation of low-rank matrix completion algorithms is that they cannot recover the case where a whole row or column is missing. The missing row or column will be simply filled as an arbitrary combination of other rows or columns with known values. This precludes the application of matrix completion to problems such as super-resolution (SR) where missing values in many rows and columns need to be recovered in the process of up-sampling a low- resolution image. Moreover, low-rank regularization considers information globally from the whole image and does not take proper consideration of local spatial consistency. Accordingly, we propose in this paper a solution to the SR problem via simultaneous (global) low-rank and (local) total variation (TV) regularization. We solve the respective cost function using the alternating direction method of multipliers (ADMM). Experiments on MR images of adults and pediatric subjects demonstrate that the proposed method enhances the details of the recovered high-resolution images, and outperforms the nearest-neighbor interpolation, cubic interpolation, non-local means, and TV-based up-sampling.
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Jian Cheng, Rachid Deriche, Tianzi Jiang, Dinggang Shen, Pew-Thian Yap,
"Non-Negative Spherical Deconvolution (NNSD) for Fiber Orientation Distribution Function Estimation",
MICCAI Workshop on Computational Diffusion MRI (CDMRI'13), pp. 81–93, 2013.
[bibtex]
[abstract]
[citations: 4]
(Oral presentation)
Bibtex
@inproceedings{cheng_NNSD_CDMRI2013, author = {Jian Cheng and Rachid Deriche and Tianzi Jiang and Dinggang Shen and Pew-Thian Yap}, booktitle = {MICCAI Workshop on Computational Diffusion MRI (CDMRI'13)}, doi = {10.1007/978-3-319-02475-2_8}, hal_id = {hal-00967829}, pages = {81-93}, pdf = {https://hal.archives-ouvertes.fr/hal-00967829/document}, title = {Non-Negative Spherical Deconvolution (NNSD) for Fiber Orientation Distribution Function Estimation}, url = {https://dx.doi.org/10.1007/978-3-319-02475-2_8}, year = {2013} }Abstract
In diffusion Magnetic Resonance Imaging (dMRI), Spherical Deconvolution (SD) is a commonly used approach for estimating the fiber Orientation Distribution Function (fODF). As a Probability Density Function (PDF) that characterizes the distribution of fiber orientations, the fODF is expected to be non-negative and to integrate to unity on the continuous unit sphere $\mathbb{S}^2$. However, many existing approaches, despite using continuous representation such as Spherical Harmonics (SH), impose non- negativity only on discretized points of $\mathbb{S}^2$. Therefore, non-negativity is not guaranteed on the whole $\mathbb{S}^2$. Existing approaches are also known to exhibit false positive fODF peaks, especially in regions with low anisotropy, causing an over- estimation of the number of fascicles that traverse each voxel. This paper proposes a novel approach, called Non-Negative SD (NNSD), to overcome the above limitations. NNSD offers the following advantages. First, NNSD is the first SH based method that guarantees non-negativity of the fODF throughout the unit sphere. Second, unlike approaches such as Maximum Entropy SD (MESD), Cartesian Tensor Fiber Orientation Distribution (CT-FOD), and discrete representation based SD (DR-SD) techniques, the SH representation allows closed form of spherical integration, efficient computation in a low dimensional space resided by the SH coefficients, and accurate peak detection on the continuous domain defined by the unit sphere. Third, NNSD is significantly less susceptible to producing false positive peaks in regions with low anisotropy. Evaluations of NNSD in comparison with Constrained SD (CSD), MESD, and DR-SD (implemented using L1-regularized least-squares with non-negative constraint), indicate that NNSD yields improved performance for both synthetic and real data. The performance gain is especially prominent for high resolution (1.25 mm)^3 data. -
Jian Cheng, Rachid Deriche, Tianzi Jiang, Dinggang Shen, Pew-Thian Yap,
"Non-Local Non-Negative Spherical Deconvolution for Single and Multiple Shell Diffusion MRI",
HARDI Reconstruction Challenge, International Symposium on Biomedical Imaging (ISBI'13), 2013.
[bibtex]
(Honorable Mention; the best technique in terms of local comparison.)
Bibtex
@conference{JianCheng_NNSD_ISBI13, author = {Jian Cheng and Rachid Deriche and Tianzi Jiang and Dinggang Shen and Pew-Thian Yap}, booktitle = {HARDI Reconstruction Challenge, International Symposium on Biomedical Imaging (ISBI'13)}, hal_id = {hal-00967830}, pdf = {https://hal.archives-ouvertes.fr/hal-00967830/document}, title = {Non-Local Non-Negative Spherical Deconvolution for Single and Multiple Shell Diffusion MRI}, url = {https://hal.archives-ouvertes.fr/hal-00967830}, year = {2013} } -
Jian Cheng, Dinggang Shen, Pew-Thian Yap,
"Non-Negative Spherical Deconvolution for Fiber Orientation Distribution Estimation",
ISMRM'13, Apr, 2013.
[bibtex]
Bibtex
@conference{cheng:ISMRM2013, address = {United States}, author = {Jian Cheng and Dinggang Shen and Pew-Thian Yap}, booktitle = {ISMRM'13}, hal_id = {hal-00967831}, month = {Apr}, pdf = {https://hal.archives-ouvertes.fr/hal-00967831/document}, title = {Non-Negative Spherical Deconvolution for Fiber Orientation Distribution Estimation}, url = {https://hal.archives-ouvertes.fr/hal-00967831}, year = {2013} }
2012
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Jian Cheng,
"Estimation and Processing of Ensemble Average Propagator and Its Features in Diffusion MRI",
PhD thesis, Université Nice Sophia Antipolis, and Chinese Academy of Sciences, May, 2012.
[bibtex]
[abstract]
[slides]
[citations: 10]
Bibtex
@phdthesis{thesis_jiancheng2012, author = {Jian Cheng}, hal_id = {tel-00759048}, month = {May}, pdf = {https://tel.archives-ouvertes.fr/tel-00759048/document}, school = {Université Nice Sophia Antipolis, and Chinese Academy of Sciences}, title = {Estimation and Processing of Ensemble Average Propagator and Its Features in Diffusion MRI}, url = {https://hal.archives-ouvertes.fr/tel-00759048}, year = {2012} }Abstract
Diffusion MRI (dMRI) is the unique technique to infer the microstructure of the white matter in vivo and noninvasively, by modeling the diffusion of water molecules. Ensemble Average Propagator (EAP) and Orientation Distribution Function (ODF) are two important Probability Density Functions (PDFs) which reflect the water diffusion. Estimation and processing of EAP and ODF is the central problem in dMRI, and is also the first step for tractography. Diffusion Tensor Imaging (DTI) is the most widely used estimation method which assumes EAP as a Gaussian distribution parameterized by a tensor. Riemannian framework for tensors has been proposed successfully in tensor estimation and processing. However, since the Gaussian EAP assumption is oversimplified, DTI can not reflect complex microstructure like fiber crossing. High Angular Resolution Diffusion Imaging (HARDI) is a category of methods proposed to avoid the limitations of DTI. Most HARDI methods like Q-Ball Imaging (QBI) need some assumptions and only can handle the data from single shell (single $b$ value), which are called as single shell HARDI (sHARDI) methods. However, with the development of scanners and acquisition methods, multiple shell data becomes more and more practical and popular. This thesis focuses on the estimation and processing methods in multiple shell HARDI (mHARDI) which can handle the diffusion data from arbitrary sampling scheme. There are many original contributions in this thesis. -First, we develop the analytical Spherical Polar Fourier Imaging (SPFI), which represents the signal using SPF basis and obtains EAP and its various features including ODFs and some scalar indices like Generalized Fractional Anisotropy (GFA) from analytical linear transforms. In the implementation of SPFI, we present two ways for scale estimation and propose to consider the prior $E(0)=1$ in estimation process. -Second, a novel Analytical Fourier Transform in Spherical Coordinate (AFT-SC) framework is proposed to incorporate many sHARDI and mHARDI methods, explore their relation and devise new analytical EAP/ODF estimation methods. -Third, we present some important criteria to compare different HARDI methods and illustrate their advantages and limitations. -Fourth, we propose a novel diffeomorphism invariant Riemannian framework for ODF and EAP processing, which is a natural generalization of previous Riemannian framework for tensors, and can be used for general PDF computing by representing the square root of the PDF called wavefunction with orthonormal basis. In this Riemannian framework, the exponential map, logarithmic map and geodesic have closed forms, the weighted Riemannian mean and median uniquely exist and can be estimated from an efficient gradient descent. Log-Euclidean framework and Affine- Euclidean framework are developed for fast data processing. -Fifth, we theoretically and experimentally compare the Euclidean metric and Riemannian metric for tensors, ODFs and EAPs. -Finally, we propose the Geodesic Anisotropy (GA) to measure the anisotropy of EAPs, Square Root Parameterized Estimation (SRPE) for nonnegative definite ODF/EAP estimation, weighted Riemannian mean/median for ODF/EAP interpolation, smoothing, atlas estimation. The concept of \emph{reasonable mean value interpolation} is presented for interpolation of general PDF data. -
Nianming Zuo, Jian Cheng, Tianzi Jiang*,
"Diffusion Magnetic Resonance Imaging for Brainnetome: A Critical Review",
Neuroscience Bulletin, vol. 28, no. 4, pp. 375–388, 2012.
[bibtex]
[abstract]
[citations: 17]
Bibtex
@article{zuo:2012, author = {Nianming Zuo and Jian Cheng and Tianzi Jiang}, doi = {10.1007/s12264-012-1245-3}, journal = {Neuroscience Bulletin}, number = {4}, pages = {375-388}, pdf = {http://www.nlpr.ia.ac.cn/2012papers/gjkw/gk84.pdf}, title = {Diffusion Magnetic Resonance Imaging for Brainnetome: A Critical Review}, url = {https://dx.doi.org/10.1007/s12264-012-1245-3}, volume = {28}, year = {2012} }Abstract
Increasing evidence shows that the human brain is a highly self- organized system that shows attributes of smallworldness, hierarchy and modularity. The "connectome" was conceived several years ago to identify the underpinning physical connectivities of brain networks. The need for an integration of multi-spatial and -temporal approaches is becoming apparent. Therefore, the "Brainnetome" (brain-net-ome) project was proposed. Diffusion magnetic resonance imaging (dMRI) is a non-invasive way to study the anatomy of brain networks. Here, we review the principles of dMRI, its methodologies, and some of its clinical applications for the Brainnetome. Future research in this field is discussed.
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Jian Cheng, Tianzi Jiang, Rachid Deriche,
"Nonnegative Definite EAP and ODF Estimation via a Unified Multi-Shell HARDI Reconstruction",
Medical Image Computing and Computer-Assisted Intervention (MICCAI'12), vol. 6892, pp. 98–106, 2012.
[bibtex]
[abstract]
[citations: 15]
(MICCAI Student Travel Award)
Bibtex
@inproceedings{cheng_MICCAI2012, author = {Jian Cheng and Tianzi Jiang and Rachid Deriche}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI'12)}, doi = {10.1007/978-3-642-33418-4_39}, pages = {98-106}, pdf = {https://hal.archives-ouvertes.fr/hal-00759014/document}, publisher = {Springer Berlin / Heidelberg}, series = {LNCS}, title = {Nonnegative Definite EAP and ODF Estimation via a Unified Multi-Shell HARDI Reconstruction}, url = {https://dx.doi.org/10.1007/978-3-642-33418-4_39}, volume = {6892}, year = {2012} }Abstract
In High Angular Resolution Diffusion Imaging (HARDI), Orientation Distribution Function (ODF) and Ensemble Average Propagator (EAP) are two important Probability Density Functions (PDFs) which reflect the water diffusion and fiber orientations. Spherical Polar Fourier Imaging (SPFI) is a recent model-free multi-shell HARDI method which estimates both EAP and ODF from the diffusion signals with multiple b values. As physical PDFs, ODFs and EAPs are nonnegative definite respectively in their domains $\mathbb{S}^2$ and $\mathbb{R}^3$. However, existing ODF/EAP estimation methods like SPFI seldom consider this natural constraint. Although some works considered the nonnegative constraint on the given discrete samples of ODF/EAP, the estimated ODF/EAP is not guaranteed to be nonnegative definite in the whole continuous domain. The Riemannian framework for ODFs and EAPs has been proposed via the square root parameterization based on pre-estimated ODFs and EAPs by other methods like SPFI. However, there is no work on how to estimate the square root of ODF/EAP called as the wavefuntion directly from diffusion signals. In this paper, based on the Riemannian framework for ODFs/EAPs and Spherical Polar Fourier (SPF) basis representation, we propose a unified model-free multi-shell HARDI method, named as Square Root Parameterized Estimation (SRPE), to simultaneously estimate both the wavefunction of EAPs and the nonnegative definite ODFs and EAPs from diffusion signals. The experiments on synthetic data and real data showed SRPE is more robust to noise and has better EAP reconstruction than SPFI, especially for EAP profiles at large radius.
2011
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Jian Cheng, Aurobrata Ghosh, Tianzi Jiang, Rachid Deriche,
"Diffeomorphism Invariant Riemannian Framework for Ensemble Average Propagator Computing",
Medical Image Computing and Computer-Assisted Intervention (MICCAI'11), vol. 6892, pp. 98–106, 2011.
[bibtex]
[abstract]
[citations: 7]
(MICCAI Student Travel Award)
Bibtex
@inproceedings{cheng_MICCAI2011, author = {Jian Cheng and Aurobrata Ghosh and Tianzi Jiang and Rachid Deriche}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI'11)}, doi = {10.1007/978-3-642-23629-7_13}, hal_id = {inria-00615431}, pages = {98-106}, pdf = {https://hal.inria.fr/inria-00615431/document}, publisher = {Springer Berlin / Heidelberg}, series = {LNCS}, title = {Diffeomorphism Invariant Riemannian Framework for Ensemble Average Propagator Computing}, url = {https://dx.doi.org/10.1007/978-3-642-23629-7_13}, volume = {6892}, year = {2011} }Abstract
Background: In Diffusion Tensor Imaging (DTI), Riemannian framework based on Information Geometry theory has been proposed for processing tensors on estimation, interpolation, smoothing, regularization, segmentation, statistical test and so on. Recently Riemannian framework has been generalized to Orientation Distribution Function (ODF) and it is applicable to any Probability Density Function (PDF) under orthonormal basis representation. Spherical Polar Fourier Imaging (SPFI) was proposed for ODF and Ensemble Average Propagator (EAP) estimation from arbitrary sampled signals without any assumption. Purpose: Tensors only can represent Gaussian EAP and ODF is the radial integration of EAP, while EAP has full information for diffusion process. To our knowledge, so far there is no work on how to process EAP data. In this paper, we present a Riemannian framework as a mathematical tool for such task. Method: We propose a state-of-the-art Riemannian framework for EAPs by representing the square root of EAP, called wavefunction based on quantum mechanics, with the Fourier dual Spherical Polar Fourier (dSPF) basis. In this framework, the exponential map, logarithmic map and geodesic have closed forms, and weighted Riemannian mean and median uniquely exist. We analyze theoretically the similarities and differences between Riemannian frameworks for EAPs and for ODFs and tensors. The Riemannian metric for EAPs is diffeomorphism invariant, which is the natural extension of the affine-invariant metric for tensors. We propose Log-Euclidean framework to fast process EAPs, and Geodesic Anisotropy (GA) to measure the anisotropy of EAPs. With this framework, many important data processing operations, such as interpolation, smoothing, atlas estimation, Principal Geodesic Analysis (PGA), can be performed on EAP data. Results and Conclusions: The proposed Riemannian framework was validated in synthetic data for interpolation, smoothing, PGA and in real data for GA and atlas estimation. Riemannian median is much robust for atlas estimation.
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Jian Cheng, Tianzi Jiang, Rachid Deriche,
"Theoretical Analysis and Practical Insights on EAP Estimation via a Unified HARDI Framework",
MICCAI Workshop on Computational Diffusion MRI (CDMRI'11), 2011.
[bibtex]
[abstract]
[project]
[citations: 24]
(Oral presentation)
Bibtex
@inproceedings{cheng_AFTSC_CDMRI2011, author = {Jian Cheng and Tianzi Jiang and Rachid Deriche}, booktitle = {MICCAI Workshop on Computational Diffusion MRI (CDMRI'11)}, hal_id = {inria-00615430}, pdf = {https://hal.inria.fr/inria-00615430/document}, title = {Theoretical Analysis and Practical Insights on EAP Estimation via a Unified HARDI Framework}, url = {https://hal.archives-ouvertes.fr/inria-00615430}, year = {2011} }Abstract
Since Diffusion Tensor Imaging (DTI) cannot describe complex non- Gaussian diffusion process, many techniques, called as single shell High Angular Resolution Diffusion Imaging (sHARDI) methods, reconstruct the Ensemble Average Propagator (EAP) or its feature Orientation Distribution Function (ODF) from diffusion weighted signals only in single shell. Q-Ball Imaging (QBI) and Diffusion Orientation Transform (DOT) are two famous sHARDI methods. However, these sHARDI methods have some intrinsic modeling errors or need some unreal assumptions. Moreover they are hard to deal with signals from different q-shells. Most recently several novel multiple shell HARDI (mHARDI) methods, including Diffusion Propagator Imaging (DPI), Spherical Polar Fourier Imaging (SPFI) and Simple Harmonic Oscillator based Reconstruction and Estimation (SHORE), were proposed to analytically estimate EAP or ODF from multiple shell (or arbitrarily sampled) signals. These three methods all represent diffusion signal with some basis functions in spherical coordinate and use plane wave formula to analytically solve the Fourier transform. To our knowledge, there is no theoretical analysis and practical comparison among these sHARDI and mHARDI methods. In this paper, we propose a unified computational framework, named Analytical Fourier Transform in Spherical Coordinate (AFT-SC), to perform such theoretical analysis and practical comparison among all these five state-of-the-art diffusion MRI methods. We compare these five methods in both theoretical and experimental aspects. With respect to the theoretical aspect, some criteria are proposed for evaluation and some differences together with some similarities among the methods are highlighted. Regarding the experimental aspect, all the methods are compared in synthetic, phantom and real data. The shortcomings and advantages of each method are highlighted from which SPFI appears to be among the best because it uses an orthonormal basis that completely separates the spherical and radial information.
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Jian Cheng, Sylvain Merlet, Emmanuel Caruyer, Aurobrata Ghosh, Tianzi Jiang, Rachid Deriche,
"Compressive Sensing Ensemble Average Propagator Estimation via L1 Spherical Polar Fourier Imaging",
MICCAI Workshop on Computational Diffusion MRI (CDMRI'11), pp. 108–118, 2011.
[bibtex]
[abstract]
[project]
[citations: 22]
(Oral presentation)
Bibtex
@inproceedings{cheng_L1SPFI_CDMRI2011, author = {Jian Cheng and Sylvain Merlet and Emmanuel Caruyer and Aurobrata Ghosh and Tianzi Jiang and Rachid Deriche}, booktitle = {MICCAI Workshop on Computational Diffusion MRI (CDMRI'11)}, hal_id = {inria-00615437}, pages = {108-118}, pdf = {https://hal.inria.fr/inria-00615437/document}, title = {Compressive Sensing Ensemble Average Propagator Estimation via L1 Spherical Polar Fourier Imaging}, url = {https://hal.archives-ouvertes.fr/inria-00615437}, year = {2011} }Abstract
Since Diffusion Tensor Imaging (DTI) cannot detect the fiber crossing, many new works beyond DTI has been proposed to explore the q-space. Most works, known as single shell High Angular Resolution Imaging (sHARDI), focus on single shell sampling and reconstruct the Orientation Distribution Function (ODF). The ODF, which has no radial information at all, is just one of features of Ensemble Average Propagator (EAP). Diffusion Spectrum Imaging (DSI) is a standard method to estimate EAP via numerical Fourier Transform (FT), which needs lots of samples and is impractical for clinical study. Spherical Polar Fourier Imaging (SPFI) [1,2] was proposed to represent the signal using SPF basis, then the EAP and the ODF have analytical closed forms. So the estimation of the coefficients under SPF basis is very important. In [1,2], the coefficients are estimated based on a standard Least Square (LS) with L2 norm regularization (L2-L2). In this paper, we propose to estimate the coefficients using LS with L1 norm regularization (L2-L1), also named as Least Absolute Selection and Shrinkage Operator (LASSO). And we prove that the L2-L1 estimation of the coefficients is actually the well known Compressive Sensing (CS) method to estimate EAP, which brings lots of Mathematical tools and possibility to improve the sampling scheme in q-space.
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Emmanuel Caruyer, Jian Cheng, Christophe Lenglet, Guillermo Sapiro, Tianzi Jiang, Rachid Deriche,
"Optimal Design of Multiple Q-shells experiments for Diffusion MRI",
MICCAI Workshop on Computational Diffusion MRI (CDMRI'11), pp. 45–53, 2011.
[bibtex]
[abstract]
[citations: 49]
(Oral presentation)
(Used in Human Connectome Project (HCP) for multi-shell diffusion MRI data acquisition.)
Bibtex
@inproceedings{caruyer_CDMRI11, author = {Emmanuel Caruyer and Jian Cheng and Christophe Lenglet and Guillermo Sapiro and Tianzi Jiang and Rachid Deriche}, booktitle = {MICCAI Workshop on Computational Diffusion MRI (CDMRI'11)}, hal_id = {inria-00617663}, pages = {45-53}, pdf = {https://hal.inria.fr/inria-00617663/document}, title = {Optimal Design of Multiple Q-shells experiments for Diffusion MRI}, url = {https://hal.archives-ouvertes.fr/inria-00617663}, year = {2011} }Abstract
Recent advances in diffusion MRI make use of the diffusion signal sampled on the whole Q-space, rather than on a single sphere. While much effort has been done to design uniform sampling schemes for single shell experiment, it is yet not clear how to build a strategy to sample the diffusion signal in the whole Fourier domain. In this article, we propose a method to generate acquisition schemes for multiple Q-shells experiment in diffusion MRI. The acquisition protocols we design are incremental, which means they remain approximately optimal when truncated before the acquisition is complete. Our method is fast, incremental, and we can generate diffusion gradients schemes for any number of acquisitions, any number of shells, and any number of points per shell. The samples arranged on different shells do not share the same directions. The method is tested for Spherical Polar Fourier reconstruction of the diffusion signal, and based on Monte-Carlo simulations, several preferred acquisition parameters are identified.
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Sylvain Merlet, Jian Cheng, Aurobrata Ghosh, Rachid Deriche,
"Spherical Polar Fourier EAP and ODF Reconstruction via Compressed Sensing in Diffusion MRI",
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'11), pp. 365–371, 2011.
[bibtex]
[abstract]
[citations: 23]
Bibtex
@inproceedings{merlet_ISBI2011, author = {Sylvain Merlet and Jian Cheng and Aurobrata Ghosh and Rachid Deriche}, booktitle = {IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'11)}, doi = {10.1109/ISBI.2011.5872425}, pages = {365-371}, pdf = {https://hal.inria.fr/inria-00585694/document}, title = {Spherical Polar Fourier EAP and ODF Reconstruction via Compressed Sensing in Diffusion MRI}, url = {https://dx.doi.org/10.1109/ISBI.2011.5872425}, year = {2011} }Abstract
In diffusion magnetic resonance imaging (dMRI), the Ensemble Average Propagator (EAP), also known as the propagator, describes completely the water molecule diffusion in the brain white matter without any prior knowledge about the tissue shape. In this paper, we describe a new and efficient method to accurately reconstruct the EAP in terms of the Spherical Polar Fourier (SPF) basis from very few diffusion weighted magnetic resonance images (DW-MRI). This approach nicely exploits the duality between SPF and a closely related basis in which one can respectively represent the EAP and the diffusion signal using the same coefficients, and efficiently combines it to the recent acquisition and reconstruction technique called Compressed Sensing (CS). Our work provides an efficient analytical solution to estimate, from few measurements, the diffusion propagator at any radius. We also provide a new analytical solution to extract an important feature characterising the tissue microstructure: the Orientation Distribution Function (ODF). We illustrate and prove the effectiveness of our method in reconstructing the propagator and the ODF on both noisy multiple q-shell synthetic and phantom data.
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Jian Cheng, Sylvain Merlet, Emmanuel Caruyer, Aurobrata Ghosh, Tianzi Jiang, Rachid Deriche,
"Compressive Sensing Ensemble Average Propagator Estimation via L1 Spherical Polar Fourier Imaging",
ISMRM'11, May, 2011.
[bibtex]
Bibtex
@conference{cheng:ISMRM2011, address = {Montréal, Canada}, author = {Jian Cheng and Sylvain Merlet and Emmanuel Caruyer and Aurobrata Ghosh and Tianzi Jiang and Rachid Deriche}, booktitle = {ISMRM'11}, hal_id = {inria-00615434}, month = {May}, pdf = {https://hal.inria.fr/inria-00615434/document}, title = {Compressive Sensing Ensemble Average Propagator Estimation via L1 Spherical Polar Fourier Imaging}, url = {https://hal.archives-ouvertes.fr/inria-00615434}, year = {2011} } -
Jian Cheng, Aurobrata Ghosh, Tianzi Jiang, Rachid Deriche,
"A Riemannian Framework for Ensemble Average Propagator Computing",
ISMRM'11, 2011.
[bibtex]
Bibtex
@conference{cheng:ISMRM2011:EAP, author = {Jian Cheng and Aurobrata Ghosh and Tianzi Jiang and Rachid Deriche}, booktitle = {ISMRM'11}, hal_id = {inria-00615436}, pdf = {https://hal.inria.fr/inria-00615436/document}, title = {A Riemannian Framework for Ensemble Average Propagator Computing}, url = {https://hal.archives-ouvertes.fr/inria-00615436}, year = {2011} }
2010
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Jian Cheng, Aurobrata Ghosh, Rachid Deriche, Tianzi Jiang,
"Model-Free, Regularized, Fast, and Robust Analytical Orientation Distribution Function Estimation",
Medical Image Computing and Computer-Assisted Intervention (MICCAI'10), vol. 6361, pp. 648–656, sep, 2010.
[bibtex]
[abstract]
[project]
[poster]
[citations: 47]
Bibtex
@inproceedings{Cheng_ODF_MICCAI2010, author = {Jian Cheng and Aurobrata Ghosh and Rachid Deriche and Tianzi Jiang}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI'10)}, doi = {10.1007/978-3-642-15705-9_79}, hal_id = {inria-00496929}, month = {sep}, pages = {648-656}, pdf = {https://hal.inria.fr/inria-00496929/document}, title = {Model-Free, Regularized, Fast, and Robust Analytical Orientation Distribution Function Estimation}, url = {https://dx.doi.org/10.1007/978-3-642-15705-9_79}, volume = {6361}, year = {2010} }Abstract
High Angular Resolution Imaging (HARDI) can better explore the complex micro-structure of white matter compared to Diffusion Tensor Imaging (DTI). Orientation Distribution Function (ODF) in HARDI is used to describe the probability of the fiber direction. There are two type definitions of the ODF, which were respectively proposed in Q-Ball Imaging (QBI) and Diffusion Spectrum Imaging (DSI). Some analytical reconstructions methods have been proposed to estimate these two type of ODFs from single shell HARDI data. However they all have some assumptions and intrinsic modeling errors. In this article, we propose, almost without any assumption, a uniform analytical method to estimate these two ODFs from DWI signals in q space, which is based on Spherical Polar Fourier Expression (SPFE) of signals. The solution is analytical and is a linear transformation from the q-space signal to the ODF represented by Spherical Harmonics (SH). It can naturally combines the DWI signals in different Q-shells. Moreover It can avoid the intrinsic Funk- Radon Transform (FRT) blurring error in QBI and it does not need any assumption of the signals, such as the multiple tensor model and mono/multi-exponential decay. We validate our method using synthetic data, phantom data and real data. Our method works well in all experiments, especially for the data with low SNR, low anisotropy and non-exponential decay.
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Jian Cheng, Aurobrata Ghosh, Tianzi Jiang, Rachid Deriche,
"Model-free and Analytical EAP Reconstruction via Spherical Polar Fourier Diffusion MRI",
Medical Image Computing and Computer-Assisted Intervention (MICCAI'10), vol. 6361, pp. 590–597, sep, 2010.
[bibtex]
[abstract]
[project]
[poster]
[citations: 89]
Bibtex
@inproceedings{Cheng_PDF_MICCAI2010, author = {Jian Cheng and Aurobrata Ghosh and Tianzi Jiang and Rachid Deriche}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI'10)}, doi = {10.1007/978-3-642-15705-9_72}, hal_id = {inria-00496932}, month = {sep}, pages = {590-597}, pdf = {https://hal.inria.fr/inria-00496932/document}, title = {Model-free and Analytical EAP Reconstruction via Spherical Polar Fourier Diffusion MRI}, url = {https://dx.doi.org/10.1007/978-3-642-15705-9_72}, volume = {6361}, year = {2010} }Abstract
How to estimate the diffusion Ensemble Average Propagator (EAP) from the DWI signals in q-space is an open problem in diffusion MRI field. Many methods were proposed to estimate the Orientation Distribution Function (ODF) that is used to describe the fiber direction. However, ODF is just one of the features of the EAP. Compared with ODF, EAP has the full information about the diffusion process which reflects the complex tissue micro-structure. Diffusion Orientation Transform (DOT) and Diffusion Spectrum Imaging (DSI) are two important methods to estimate the EAP from the signal. However, DOT is based on mono-exponential assumption and DSI needs a lot of samplings and very large b values. In this paper, we propose Spherical Polar Fourier Imaging (SPFI), a novel model-free fast robust analytical EAP reconstruction method, which almost does not need any assumption of data and does not need too many samplings. SPFI naturally combines the DWI signals with different b-values. It is an analytical linear transformation from the q-space signal to the EAP profile represented by Spherical Harmonics (SH). We validated the proposed methods in synthetic data, phantom data and real data. It works well in all experiments, especially for the data with low SNR, low anisotropy, and non-exponential decay.
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Jian Cheng, Aurobrata Ghosh, Tianzi Jiang, Rachid Deriche,
"Fast, Model-Free, Analytical ODF Reconstruction from the Q-Space Signal",
Proceedings of the Sixteenth Annual Meeting of the Organization for Human Brain Mapping (OHBM'10), 2010.
[bibtex]
Bibtex
@conference{Cheng_ODF_OHBM2010, author = {Jian Cheng and Aurobrata Ghosh and Tianzi Jiang and Rachid Deriche}, booktitle = {Proceedings of the Sixteenth Annual Meeting of the Organization for Human Brain Mapping (OHBM'10)}, hal_id = {inria-00497243}, pdf = {https://hal.inria.fr/inria-00497243/document}, title = {Fast, Model-Free, Analytical ODF Reconstruction from the Q-Space Signal}, url = {https://hal.archives-ouvertes.fr/inria-00497243}, year = {2010} } -
Jian Cheng, Aurobrata Ghosh, Tianzi Jiang, Rachid Deriche,
"Fast, model-free, analytical diffusion PDF profile estimation from the DWI signals",
Proceedings of the Sixteenth Annual Meeting of the Organization for Human Brain Mapping (OHBM'10), 2010.
[bibtex]
Bibtex
@conference{Cheng_PDF_OHBM2010, author = {Jian Cheng and Aurobrata Ghosh and Tianzi Jiang and Rachid Deriche}, booktitle = {Proceedings of the Sixteenth Annual Meeting of the Organization for Human Brain Mapping (OHBM'10)}, hal_id = {inria-00497244}, pdf = {https://hal.inria.fr/inria-00497244/document}, title = {Fast, model-free, analytical diffusion PDF profile estimation from the DWI signals}, url = {https://hal.archives-ouvertes.fr/inria-00497244}, year = {2010} } -
Jian Cheng, Aurobrata Ghosh, Tianzi Jiang, Rachid Deriche,
"Riemannian Median and Its Applications for Orientation Distribution Function Computing",
ISMRM'10, 2010.
[bibtex]
Bibtex
@conference{JianChengISMRM2010, author = {Jian Cheng and Aurobrata Ghosh and Tianzi Jiang and Rachid Deriche}, booktitle = {ISMRM'10}, hal_id = {inria-00497246}, pdf = {https://hal.inria.fr/inria-00497246/document}, title = {Riemannian Median and Its Applications for Orientation Distribution Function Computing}, url = {https://hal.archives-ouvertes.fr/inria-00497246}, year = {2010} }
2009
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Jian Cheng, Aurobrata Ghosh, Tianzi Jiang, Rachid Deriche,
"A Riemannian Framework for Orientation Distribution Function Computing",
Medical Image Computing and Computer-Assisted Intervention (MICCAI'09), vol. 5761, pp. 911–918, 2009.
[bibtex]
[abstract]
[citations: 52]
Bibtex
@inproceedings{JianChengMICCAI2009, author = {Jian Cheng and Aurobrata Ghosh and Tianzi Jiang and Rachid Deriche}, booktitle = {Medical Image Computing and Computer-Assisted Intervention (MICCAI'09)}, doi = {10.1007/978-3-642-04268-3_112}, hal_id = {inria-00424764}, pages = {911-918}, pdf = {https://hal.inria.fr/inria-00424764/document}, title = {A Riemannian Framework for Orientation Distribution Function Computing}, url = {https://dx.doi.org/10.1007/978-3-642-04268-3_112}, volume = {5761}, year = {2009} }Abstract
Compared with Diffusion Tensor Imaging (DTI), High Angular Resolution Imaging (HARDI) can better explore the complex microstructure of white matter. Orientation Distribution Function (ODF) is used to describe the probability of the fiber direction. Fisher information metric has been constructed for probability density family in Information Geometry theory and it has been successfully applied for tensor computing in DTI. In this paper, we present a state of the art Riemannian framework for ODF computing based on Information Geometry and sparse representation of orthonormal bases. In this Riemannian framework, the exponential map, logarithmic map and geodesic have closed forms. And the weighted Frechet mean exists uniquely on this manifold. We also propose a novel scalar measurement, named Geometric Anisotropy (GA), which is the Riemannian geodesic distance between the ODF and the isotropic ODF. The Renyi entropy $H_{1/2}$ of the ODF can be computed from the GA. Moreover, we present an Affine-Euclidean framework and a Log-Euclidean framework so that we can work in an Euclidean space. As an application, Lagrange interpolation on ODF field is proposed based on weighted Frechet mean. We validate our methods on synthetic and real data experiments. Compared with existing Riemannian frameworks on ODF, our framework is model-free. The estimation of the parameters, i.e. Riemannian coordinates, is robust and linear. Moreover it should be noted that our theoretical results can be used for any probability density function (PDF) under an orthonormal basis representation. -
Jian Cheng, Feng Shi, Kun Wang, Ming Song, Jiefeng Jiang, Lijuan Xu, Tianzi Jiang,
"Nonparametric Mean Shift Functional Detection in the Functional Space for Task and Resting-state fMRI",
Workshop on fMRI data analysis: statistical modeling and detection issues in intra- and inter-subject functional MRI data analysis in conjunction with the MICCAI, 2009.
[bibtex]
[abstract]
[citations: 3]
(Oral presentation)
Bibtex
@inproceedings{Cheng_workshopMICCAI2009, author = {Jian Cheng and Feng Shi and Kun Wang and Ming Song and Jiefeng Jiang and Lijuan Xu and Tianzi Jiang}, booktitle = {Workshop on fMRI data analysis: statistical modeling and detection issues in intra- and inter-subject functional MRI data analysis in conjunction with the MICCAI}, hal_id = {inria-00424765}, pdf = {https://hal.inria.fr/inria-00424765/document}, title = {Nonparametric Mean Shift Functional Detection in the Functional Space for Task and Resting-state fMRI}, url = {https://hal.archives-ouvertes.fr/inria-00424765}, year = {2009} }Abstract
In functional Magnetic Resonance Imaging (fMRI) data analysis, normalization of time series is an important and sometimes necessary preprocessing step in many widely used methods. The space of normalized time series with n time points is the unit sphere $S^{n-2}$, named the functional space. Riemannian framework on the sphere, including the geodesic, the exponential map, and the logarithmic map, has been well studied in Riemannian geometry. In this paper, by introducing the Riemannian framework in the functional space, we propose a novel nonparametric robust method, namely Mean Shift Functional Detection (MSFD), to explore the functional space. The first merit of the MSFD is that it does not need many assumptions on data which are assumed in many existing method, e.g. linear addition (GLM, PCA, ICA), uncorrelation (PCA), independence (ICA), the number and the shape of clusters (FCM). Second, MSFD takes into account the spatial information and can be seen as a multivariate extension of the functional connectivity analysis method. It is robust and works well for activation detection in task study even with a biased activation reference. It is also able to find the functional networks in resting-state study without a user-selected "seed" region. Third, it can enhance the boundary between different functional networks. Experiments were conducted on synthetic and real data to compare the performance of the proposed method with GLM and ICA. The experimental results validated the accuracy and robustness of MSFD, not only for activation detection in task study but also for functional network exploration in resting-state study.
2008
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Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, Mingliang Zhu, Jian Cheng,
"Visual tracking via incremental log-Euclidean Riemannian subspace learning",
IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08), 2008.
[bibtex]
[abstract]
[citations: 172]
Bibtex
@inproceedings{li_cheng_CVPR2008, author = {Xi Li and Weiming Hu and Zhongfei Zhang and Xiaoqin Zhang and Mingliang Zhu and Jian Cheng}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08)}, doi = {10.1109/CVPR.2008.4587516}, organization = {IEEE}, pdf = {http://www.nlpr.ia.ac.cn/2008papers/gjhy/gh9.pdf}, title = {Visual tracking via incremental log-Euclidean Riemannian subspace learning}, url = {https://dx.doi.org/10.1109/CVPR.2008.4587516}, year = {2008} }Abstract
Recently, a novel Log-Euclidean Riemannian metric [28] is proposed for statistics on symmetric positive definite (SPD) matrices. Under this metric, distances and Riemannian means take a much simpler form than the widely used affine-invariant Riemannian metric. Based on the Log-Euclidean Riemannian metric, we develop a tracking framework in this paper. In the framework, the covariance matrices of image features in the five modes are used to represent object appearance. Since a nonsingular covariance matrix is a SPD matrix lying on a connected Riemannian manifold, the Log-Euclidean Riemannian metric is used for statistics on the covariance matrices of image features. Further, we present an effective online Log-Euclidean Riemannian subspace learning algorithm which models the appearance changes of an object by incrementally learning a low-order Log-Euclidean eigenspace representation through adaptively updating the sample mean and eigenbasis. Tracking is then led by the Bayesian state inference framework in which a particle filter is used for propagating sample distributions over the time. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed framework.