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: 1640 •  H-Index: 21

• TMI (4) • MedIA (2) • eLife (1) • NeuroImage (2) • HBM (5) • CC (1) • CVPR (1) • MICCAI (13) • IPMI (2) • 

• 2024 • 2023 • 2022 • 2021 • 2020 • 2018 • 2017 • 2016 • 2015 • 2014 • 2013 • 2012 • 2011 • 2010 • 2009 • 2008 • 

2024

1. 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: 6] 
   ×

   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}
   }

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   ×

   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.

   Close


2. 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] 
   ×

   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}
   }

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   ×

   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

   Close


3. 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: 6] 
   ×

   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}
   }

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   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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4. 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, 2024.
     [bibtex]  [abstract]  [citations: 1] 
   ×

   Bibtex

   @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},
    journal = {Journal of Neural Engineering},
    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},
    year = {2024}
   }

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   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.

   Close


5. 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}
   }

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   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.

   Close


6. 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: 1] 
   ×

   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}
   }

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   ×

   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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7. 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] 
   ×

   Bibtex

   @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}
   }

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   ×

   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

   Close

2023

1. 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: 24] 
   ×

   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}
   }

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   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.

   Close


2. 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}
   }

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   ×

   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.}

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3. 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: 7] 
   ×

   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}
   }

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   ×

   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.}

   Close


4. 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: 6] 
   ×

   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}
   }

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   ×

   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.

   Close


5. 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}
   }

   Close
   ×

   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.

   Close


6. 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: 9] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


7. 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, pp. 1–7, 2023.
     [bibtex]  [abstract] 
   ×

   Bibtex

   @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},
    pages = {1–7},
    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},
    year = {2023}
   }

   Close
   ×

   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.

   Close


8. 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: 5] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


9. 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}
   }

   Close
   ×

   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.

   Close


10. 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: 2] 
    ×

    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}
    }

    Close
    ×

    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.

    Close

2022

1. 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}
   }

   Close
   ×

   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.

   Close


2. 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: 15] 
   ×

   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}
   }

   Close
   ×

   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).

   Close


3. 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: 9] 
   ×

   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}
   }

   Close
   ×

   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.

   Close

2021

1. 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: 63]  (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}
   }

   Close
   ×

   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.

   Close


2. 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: 14] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


3. 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: 9]  (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}
   }

   Close
   ×

   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.

   Close


4. 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: 17] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


5. 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: 28] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


6. 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: 8] 
   ×

   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}
   }

   Close
   ×

   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.

   Close

2020

1. 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: 15] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


2. 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: 24] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


3. 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: 7] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


4. 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: 6]  (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}
   }

   Close
   ×

   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.

   Close

2018

1. 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: 33] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


2. 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: 13] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


3. 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: 40] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


4. 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}
   }

   Close
   ×

   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.

   Close

2017

1. 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: 26] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


2. 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}
   }

   Close
   ×

   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.

   Close


3. 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}
   }

   Close

2016

1. 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}
   }

   Close
   ×

   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.

   Close


2. 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}
   }

   Close
   ×

   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.

   Close


3. 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}
   }

   Close
   ×

   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.

   Close

2015

1. 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: 327]  (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}
   }

   Close
   ×

   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.

   Close


2. 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}
   }

   Close
   ×

   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.

   Close


3. 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: 8]  (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}
   }

   Close
   ×

   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.

   Close


4. 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: 29] 
   ×

   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}
   }

   Close
   ×

   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).

   Close


5. 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: 14]  (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}
   }

   Close
   ×

   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.

   Close


6. 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}
   }

   Close

2014

1. 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}
   }

   Close
   ×

   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.

   Close


2. 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: 87] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


3. 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 Interventio (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 Interventio (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}
   }

   Close
   ×

   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).

   Close


4. 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}
   }

   Close
   ×

   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.

   Close


5. 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}
   }

   Close
   ×

   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

   Close


6. 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}
   }

   Close


7. 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}
   }

   Close

2013

1. 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}
   }

   Close
   ×

   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.

   Close


2. 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: 50]  (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}
   }

   Close
   ×

   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.

   Close


3. 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}
   }

   Close
   ×

   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.

   Close


4. 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}
   }

   Close


5. 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}
   }

   Close

2012

1. 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}
   }

   Close
   ×

   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.

   Close


2. 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}
   }

   Close
   ×

   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.

   Close


3. 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: 13]  (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}
   }

   Close
   ×

   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.

   Close

2011

1. 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: 6]  (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}
   }

   Close
   ×

   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.

   Close


2. 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}
   }

   Close
   ×

   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.

   Close


3. 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: 18]  (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}
   }

   Close
   ×

   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.

   Close


4. 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: 50]  (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}
   }

   Close
   ×

   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.

   Close


5. 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: 21] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


6. 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}
   }

   Close


7. 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}
   }

   Close

2010

1. 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}
   }

   Close
   ×

   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.

   Close


2. 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: 91] 
   ×

   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}
   }

   Close
   ×

   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.

   Close


3. 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}
   }

   Close


4. 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}
   }

   Close


5. 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}
   }

   Close

2009

1. 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: 54] 
   ×

   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}
   }

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   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.

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2. 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}
   }

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   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.

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2008

1. 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] 
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   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}
   }

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   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.

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