Publications By Type

 

• COPYRIGHT NOTICE: These materials are presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders.
• 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) • 

• Book Chapters • Journals • Conferences • Abstracts • Theses • 

Book Chapters

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}
   }

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   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

Journal Articles

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.

   Close


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.

   Close


7. 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


8. 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.}

   Close


9. 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


10. 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


11. 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


12. 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


13. 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


14. 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


15. 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}
    }

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    ×

    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


16. 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


17. 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


18. 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


19. 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


20. 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


21. 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


22. 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


23. 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}
    }

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    ×

    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


24. 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}
    }

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    ×

    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


25. 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


26. 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


27. 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}
    }

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    ×

    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


28. 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}
    }

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    ×

    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


29. 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


30. 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


31. 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


32. 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


33. 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


34. 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


35. 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


36. 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


37. 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


38. 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

Conference Articles

1. 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}
   }

   Close
   ×

   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


2. 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


3. 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


4. 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


5. 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


6. 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


7. 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}
   }

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   ×

   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


8. 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


9. 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


10. 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


11. 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


12. 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


13. 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


14. 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


15. 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


16. 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


17. 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


18. 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


19. 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


20. 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


21. 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


22. 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


23. 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}
    }

    Close
    ×

    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.

    Close


24. 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}
    }

    Close
    ×

    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.

    Close


25. Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, Mingliang Zhu, Jian Cheng,
    "Visual tracking via incremental log-Euclidean Riemannian subspace learning",
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08), 2008.
        [bibtex]  [abstract]  [citations: 172] 
    ×

    Bibtex

    @inproceedings{li_cheng_CVPR2008,
     author = {Xi Li and Weiming Hu and Zhongfei Zhang and Xiaoqin Zhang and Mingliang Zhu and Jian Cheng},
     booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08)},
     doi = {10.1109/CVPR.2008.4587516},
     organization = {IEEE},
     pdf = {http://www.nlpr.ia.ac.cn/2008papers/gjhy/gh9.pdf},
     title = {Visual tracking via incremental log-Euclidean Riemannian subspace learning},
     url = {https://dx.doi.org/10.1109/CVPR.2008.4587516},
     year = {2008}
    }

    Close
    ×

    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.

    Close

Conference Abstracts

1. 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


2. 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


3. 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


4. 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


5. 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


6. 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


7. 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


8. 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


9. 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}
   }

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10. 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}
    }

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11. 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}
    }

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Theses

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}
   }

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   ×

   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.

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