Publications By Selected Venues¶

 

• Total Citations: 1464 •  H-Index: 19

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

TMI

1. Yan Wang, Jian Cheng*, Yixin Chen, Shuai Shao, Lanyun Zhu, Zhenzhou Wu, Tao Liu, Haogang Zhu*,
   "FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation",
   IEEE Transactions on Medical Imaging (TMI), vol. 42, no. 12, pp. 3738–3751, 2023.
   
       [bibtex]  [abstract]  [arxiv]  [citations: 8] 
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   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.

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2. 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: 47]  (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}
   }

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   Abstract

   Chronological age of healthy people is able to be predicted
   accurately using deep neural networks from neuroimaging data,  and
   the predicted brain age could serve as a biomarker for detecting
   aging-related diseases.  In this paper, a novel 3D convolutional
   network, called two-stage-age-network (TSAN), is proposed to
   estimate brain age from T1-weighted MRI data.  Compared with
   existing methods, TSAN has the following improvements.  First, TSAN
   uses a two-stage cascade network architecture, where the first-stage
   network estimates a rough brain age,  then the second-stage network
   estimates the brain age more accurately from the discretized brain
   age by the first-stage network.  Second, to our knowledge, TSAN is
   the first work to apply novel ranking losses in brain age
   estimation, together with the traditional mean square error (MSE)
   loss.  Third, densely connected paths are used to combine feature
   maps with different scales.  The experiments with $6586$ MRIs showed
   that TSAN could provide accurate brain age estimation,  yielding
   mean absolute error (MAE) of $2.428$ and Pearson's correlation
   coefficient (PCC) of $0.985$, between the estimated and
   chronological ages.  Furthermore, using the brain age gap between
   brain age and chronological age as a biomarker,  Alzheimer's disease
   (AD) and Mild Cognitive Impairment (MCI) can be distinguished from
   healthy control (HC) subjects by support vector machine (SVM).
   Classification AUC in AD/HC and MCI/HC was $0.904$ and $0.823$,
   respectively.  It showed that brain age gap is an effective
   biomarker associated with risk of dementia, and has potential for
   early-stage dementia risk screening.  The codes and trained models
   have been released on GitHub: https://github.com/Milan-BUAA/TSAN-
   brain-age-estimation.

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3. 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: 26] 
   ×

   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}
   }

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   Abstract

   In diffusion MRI (dMRI), a good sampling scheme is important for
   efficient acquisition and robust reconstruction. Diffusion weighted
   signal is normally acquired on single or multiple shells in q-space.
   Signal samples are typically distributed uniformly on different
   shells to make them invariant to the orientation of structures
   within tissue, or the laboratory coordinate frame. The Electrostatic
   Energy Minimization (EEM) method, originally proposed for single
   shell sampling scheme in dMRI, was recently generalized to multi-
   shell schemes, called Generalized EEM (GEEM). GEEM has been
   successfully used in the Human Connectome Project (HCP). However,
   EEM does not directly address the goal of optimal sampling, i.e.,
   achieving large angular separation between sampling points. In this
   paper, we propose a more natural formulation, called Spherical Code
   (SC), to directly maximize the minimal angle between different
   samples in single or multiple shells. We consider not only
   continuous problems to design single or multiple shell sampling
   schemes, but also discrete problems to uniformly extract sub-sampled
   schemes from an existing single or multiple shell scheme, and to
   order samples in an existing scheme. We propose five algorithms to
   solve the above problems, including an incremental SC (ISC), a
   sophisticated greedy algorithm called Iterative Maximum Overlap
   Construction (IMOC), an 1-Opt greedy method, a Mixed Integer Linear
   Programming (MILP) method, and a Constrained Non-Linear Optimization
   (CNLO) method. To our knowledge, this is the first work to use the
   SC formulation for single or multiple shell sampling schemes in
   dMRI. Experimental results indicate that SC methods obtain larger
   angular separation and better rotational invariance than the state-
   of-the-art EEM and GEEM. The related codes and a tutorial have been
   released in DMRITool.

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4. 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: 299]  (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}
   }

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   Abstract

   Image super-resolution (SR) aims to recover high-resolution images
   from their low-resolution counterparts for improving image analysis
   and visualization. Interpolation methods, widely used for this
   purpose, often result in images with blurred edges and blocking
   effects. More advanced methods such as total variation (TV) retain
   edge sharpness during image recovery. However, these methods only
   utilize information from local neighborhoods, neglecting useful
   information from remote voxels. In this paper, we propose a novel
   image SR method that integrates both local and global information
   for effective image recovery. This is achieved by, in addition to
   TV, low-rank regularization that enables utilization of information
   throughout the image. The optimization problem can be solved
   effectively via alternating direction method of multipliers (ADMM).
   Experiments on MR images of both adult and pediatric subjects
   demonstrate that the proposed method enhances the details in the
   recovered high-resolution images, and outperforms methods such as
   the nearest-neighbor interpolation, cubic interpolation, iterative
   back projection (IBP), non-local means (NLM), and TV-based up-
   sampling.

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MedIA

1. Shengke Xue#, Zhaowei Cheng#, Guangxu Han, Chaoliang Sun, Ke Fang, Yingchao Liu, Jian Cheng, Xinyu Jin, Ruiliang Bai*,
   "2D probabilistic undersampling pattern optimization for MR image reconstruction",
   Medical Image Analysis (MedIA), vol. 77, pp. 102346, 2022.
   
     [bibtex]  [abstract]  [citations: 6] 
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   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}
   }

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   Abstract

   With 3D magnetic resonance imaging (MRI), a tradeoff exists between
   higher image quality and shorter scan time. One way to solve this
   problem is to reconstruct high-quality MRI images from undersampled
   k-space. There have been many recent studies exploring effective
   k-space undersampling patterns and designing MRI reconstruction
   methods from undersampled k-space, which are two necessary steps.
   Most studies separately considered these two steps, although in
   theory, their performance is dependent on each other. In this study,
   we propose a joint optimization model, trained end-to-end, to
   simultaneously optimize the undersampling pattern in the Fourier
   domain and the reconstruction model in the image domain. A 2D
   probabilistic undersampling layer was designed to optimize the
   undersampling pattern and probability distribution in a
   differentiable manner. A 2D inverse Fourier transform layer was
   implemented to connect the Fourier domain and the image domain
   during the forward and back propagation. Finally, we discovered an
   optimized relationship between the probability distribution of the
   undersampling pattern and its corresponding sampling rate. Further
   testing was performed using 3D T1-weighted MR images of the brain
   from the MICCAI 2013 Grand Challenge on Multi-Atlas Labeling dataset
   and locally acquired brain 3D T1-weighted MR images of healthy
   volunteers and contrast-enhanced 3D T1-weighted MR images of high-
   grade glioma patients. The results showed that the recovered MR
   images using our 2D probabilistic undersampling pattern (with or
   without the reconstruction network) significantly outperformed those
   using the existing start-of-the-art undersampling strategies for
   both qualitative and quantitative comparison, suggesting the
   advantages and some extent of the generalization of our proposed
   method.

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2. Jian Cheng*, Peter J. Basser*,
   "Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in diffusion MRI",
   Medical Image Analysis (MedIA), vol. 43, pp. 112–128, 2018.
   
       [bibtex]  [abstract]  [arxiv]  [citations: 11] 
   ×

   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}
   }

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   Abstract

   In Diffusion Tensor Imaging (DTI) or High Angular Resolution
   Diffusion Imaging (HARDI), a tensor field or a spherical function
   field (e.g., an orientation distribution function field), can be
   estimated from measured diffusion weighted images. In this paper,
   inspired by the microscopic theoretical treatment of phases in
   liquid crystals, we introduce a novel mathematical framework, called
   Director Field Analysis (DFA), to study local geometric structural
   information of white matter based on the reconstructed tensor field
   or spherical function field: 1) We propose a set of mathematical
   tools to process general director data, which consists of dyadic
   tensors that have orientations but no direction. 2) We propose
   Orientational Order (OO) and Orientational Dispersion (OD) indices
   to describe the degree of alignment and dispersion of a spherical
   function in a single voxel or in a region, respectively; 3) We also
   show how to construct a local orthogonal coordinate frame in each
   voxel exhibiting anisotropic diffusion; 4) Finally, we define three
   indices to describe three types of orientational distortion (splay,
   bend, and twist) in a local spatial neighborhood, and a total
   distortion index to describe distortions of all three types. To our
   knowledge, this is the first work to quantitatively describe
   orientational distortion (splay, bend, and twist) in general
   spherical function fields from DTI or HARDI data. The proposed DFA
   and its related mathematical tools can be used to process not only
   diffusion MRI data but also general director field data, and the
   proposed scalar indices are useful for detecting local geometric
   changes of white matter for voxel-based or tract-based analysis in
   both DTI and HARDI acquisitions. The related codes and a tutorial
   for DFA will be released in DMRITool.

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NeuroImage

1. 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: 12] 
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   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}
   }

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   Abstract

   The relationships between aging and brain morphology have been
   reported in many previous structural brain studies. However, the
   trajectories of successful brain aging in the extremely old remain
   underexplored. In the limited research on the oldest old, covering
   individuals aged 85 years and older, there are very few studies that
   have focused on the cortical morphology, especially cortical sulcal
   features. In this paper, we measured sulcal width and depth as well
   as cortical thickness from T1-weighted scans of 290 nondemented
   community-dwelling participants aged between 76 and 103 years. We
   divided the participants into young old (between 76 and 84; mean =
   80.35±2.44; male/female = 76/88) and oldest old (between 85 and 103;
   mean = 91.74±5.11; male/female = 60/66) groups. The results showed
   that most of the examined sulci significantly widened with increased
   age and that the rates of sulcal widening were lower in the oldest
   old. The spatial pattern of the cortical thinning partly
   corresponded with that of sulcal widening. Compared to females,
   males had significantly wider sulci, especially in the oldest old.
   This study builds a foundation for future investigations of
   neurocognitive disorders and neurodegenerative diseases in the
   oldest old, including centenarians.

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2. 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: 41]  (Honorable Mention in ISBI HARDI Reconstruction Challenge in 2013; the best technique in terms of local comparison.) 
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   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}
   }

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   Abstract

   Spherical Deconvolution (SD) is commonly used for estimating fiber
   Orientation Distribution Functions (fODFs) from diffusion-weighted
   signals. Existing SD methods can be classified into two categories:
   1) Continuous Representation based SD (CR-SD), where typically
   Spherical Harmonic (SH) representation is used for convenient
   analytical solutions, and 2) Discrete Representation based SD (DR-
   SD), where the signal profile is represented by a discrete set of
   basis functions uniformly oriented on the unit sphere. A feasible
   fODF should be non-negative and should integrate to unity throughout
   the unit sphere S 2. However, to our knowledge, most existing SH-
   based SD methods enforce non-negativity only on discretized points
   and not the whole continuum of S 2. Maximum Entropy SD (MESD) and
   Cartesian Tensor Fiber Orientation Distributions (CT-FOD) are the
   only SD methods that ensure non-negativity throughout the unit
   sphere. They are however computational intensive and are susceptible
   to errors caused by numerical spherical integration. Existing SD
   methods are also known to overestimate the number of fiber
   directions, especially in regions with low anisotropy. DR-SD
   introduces additional error in peak detection owing to the angular
   discretization of the unit sphere. This paper proposes a SD
   framework, called Non-Negative SD (NNSD), to overcome all the
   limitations above. NNSD is significantly less susceptible to the
   false-positive peaks, uses SH representation for efficient
   analytical spherical deconvolution, and allows accurate peak
   detection throughout the whole unit sphere. We further show that
   NNSD and most existing SD methods can be extended to work on multi-
   shell data by introducing a three-dimensional fiber response
   function. We evaluated NNSD in comparison with Constrained SD (CSD),
   a quadratic programming variant of CSD, MESD, and an L1-norm
   regularized non-negative least-squares DR-SD. Experiments on
   synthetic and real single-/multi-shell data indicate that NNSD
   improves estimation performance in terms of mean difference of
   angles, peak detection consistency, and anisotropy contrast between
   isotropic and anisotropic regions.

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HBM

1. 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] 
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   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

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2. 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: 5]  (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}
   }

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   Abstract

   Abstract White matter abnormalities represent early
   neuropathological events in neurodegenerative diseases such as
   Alzheimer's disease (AD), investigating these white matter
   alterations would likely provide valuable insights into pathological
   changes over the course of AD. Using a novel mathematical framework
   called “Director Field Analysis” (DFA), we investigated the
   geometric microstructural properties (i.e., splay, bend, twist, and
   total distortion) in the orientation of white matter fibers in AD,
   amnestic mild cognitive impairment (aMCI), and cognitively normal
   (CN) individuals from the Alzheimer's Disease Neuroimaging
   Initiative 2 database. Results revealed that AD patients had
   extensive orientational changes in the bilateral anterior thalamic
   radiation, corticospinal tract, inferior and superior longitudinal
   fasciculus, inferior fronto-occipital fasciculus, and uncinate
   fasciculus in comparison with CN. We postulate that these
   orientational changes of white matter fibers may be partially caused
   by the expansion of lateral ventricle, white matter atrophy, and
   gray matter atrophy in AD. In contrast, aMCI individuals showed
   subtle orientational changes in the left inferior longitudinal
   fasciculus and right uncinate fasciculus, which showed a significant
   association with the cognitive performance, suggesting that these
   regions may be preferential vulnerable to breakdown by
   neurodegenerative brain disorders, thereby resulting in the
   patients' cognitive impairment. To our knowledge, this article is
   the first to examine geometric microstructural changes in the
   orientation of white matter fibers in AD and aMCI. Our findings
   demonstrate that the orientational information of white matter
   fibers could provide novel insight into the underlying biological
   and pathological changes in AD and aMCI.

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3. 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: 9] 
   ×

   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


4. 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: 19] 
   ×

   Bibtex

   @article{li:HBM2021,
    author = {Xinxin Li and Yu Zhao and Jiyang Jiang and Jian Cheng and Wanlin Zhu and Zhenzhou Wu and Jing Jing and Zhe Zhang and Wei Wen and Perminder S. Sachdev and Yongjun Wang and Tao Liu and Zixiao Li},
    doi = {10.1002/hbm.25695},
    journal = {Human Brain Mapping},
    pdf = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.25695},
    title = {White matter hyperintensities segmentation using an ensemble of neural networks},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.25695},
    year = {2021}
   }

   Close
   ×

   Abstract

   Abstract White matter hyperintensities (WMHs) represent the most
   common neuroimaging marker of cerebral small vessel disease (CSVD).
   The volume and location of WMHs are important clinical measures. We
   present a pipeline using deep fully convolutional network and
   ensemble models, combining U-Net, SE-Net, and multi-scale features,
   to automatically segment WMHs and estimate their volumes and
   locations. We evaluated our method in two datasets: a clinical
   routine dataset comprising 60 patients (selected from Chinese
   National Stroke Registry, CNSR) and a research dataset composed of
   60 patients (selected from MICCAI WMH Challenge, MWC). The
   performance of our pipeline was compared with four freely available
   methods: LGA, LPA, UBO detector, and U-Net, in terms of a variety of
   metrics. Additionally, to access the model generalization ability,
   another research dataset comprising 40 patients (from Older
   Australian Twins Study and Sydney Memory and Aging Study, OSM), was
   selected and tested. The pipeline achieved the best performance in
   both research dataset and the clinical routine dataset with DSC
   being significantly higher than other methods (p < .001), reaching
   .833 and .783, respectively. The results of model generalization
   ability showed that the model trained on the research dataset
   (DSC = 0.736) performed higher than that trained on the clinical
   dataset (DSC = 0.622). Our method outperformed widely used pipelines
   in WMHs segmentation. This system could generate both image and text
   outputs for whole brain, lobar and anatomical automatic labeling
   WMHs. Additionally, software and models of our method are made
   publicly available at https://www.nitrc.org/projects/what\_v1.

   Close


5. 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: 81] 
   ×

   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

CC

1. 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: 5] 
   ×

   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}
   }

   Close
   ×

   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

CVPR

1. Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, Mingliang Zhu, Jian Cheng,
   "Visual tracking via incremental log-Euclidean Riemannian subspace learning",
   IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08), 2008.
   
       [bibtex]  [abstract]  [citations: 173] 
   ×

   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

MICCAI

1. 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: 5]  (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


2. 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: 1] 
   ×

   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


3. 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: 14] 
   ×

   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


4. 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: 7]  (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


5. 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: 22]  (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


6. 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: 13] 
   ×

   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


7. 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


8. 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


9. 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


10. 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


11. 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: 48] 
    ×

    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


12. 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


13. 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: 53] 
    ×

    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

IPMI

1. 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


2. 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: 27] 
   ×

   Bibtex

   @inproceedings{cheng_IPMI2015,
    author = {Jian Cheng and Dinggang Shen and Peter J. Basser and Pew-Thian Yap},
    booktitle = {International Conference on Information Processing in Medical Imaging (IPMI'15)},
    doi = {10.1007/978-3-319-19992-4_62},
    organization = {Springer},
    pages = {782-793},
    pdf = {https://hal.archives-ouvertes.fr/hal-01356133/document},
    title = {Joint 6D k-q Space Compressed Sensing for Accelerated High Angular Resolution Diffusion MRI},
    url = {https://dx.doi.org/10.1007/978-3-319-19992-4_62},
    volume = {9123},
    year = {2015}
   }

   Close
   ×

   Abstract

   High Angular Resolution Diffusion Imaging (HARDI) avoids the
   Gaussian diffusion assumption that is inherent in Diffusion Tensor
   Imaging (DTI), and is capable of characterizing complex white matter
   micro-structure with greater precision. However, HARDI methods such
   as Diffusion Spectrum Imaging (DSI) typically require significantly
   more signal measurements than DTI, resulting in prohibitively long
   scanning times. One of the goals in HARDI research is therefore to
   improve estimation of quantities such as the Ensemble Average
   Propagator (EAP) and the Orientation Distribution Function (ODF)
   with a limited number of diffusion-weighted measurements. A popular
   approach to this problem, Compressed Sensing (CS), affords highly
   accurate signal reconstruction using significantly fewer (sub-
   Nyquist) data points than required traditionally. Existing
   approaches to CS diffusion MRI (CS-dMRI) mainly focus on applying CS
   in the q-space of diffusion signal measurements and fail to take
   into consideration information redundancy in the k-space. In this
   paper, we propose a framework, called 6-Dimensional Compressed
   Sensing diffusion MRI (6D-CS-dMRI), for reconstruction of the
   diffusion signal and the EAP from data sub-sampled in both 3D
   k-space and 3D q-space. To our knowledge, 6D-CS-dMRI is the first
   work that applies compressed sensing in the full 6D k-q space and
   reconstructs the diffusion signal in the full continuous q-space and
   the EAP in continuous displacement space. Experimental results on
   synthetic and real data demonstrate that, compared with full DSI
   sampling in k-q space, 6D-CS-dMRI yields excellent diffusion signal
   and EAP reconstruction with low root-mean-square error (RMSE) using
   11 times less samples (3-fold reduction in k-space and 3.7-fold
   reduction in q-space).

   Close