東華大學圖書館 |

Computational diffusion MRI = International MICCAI Workshop, Lima, Peru, October 2020 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational diffusion MRI/ edited by Noemi Gyori ... [et al.].
其他題名:	International MICCAI Workshop, Lima, Peru, October 2020 /
其他作者:	Gyori, Noemi.
團體作者:	International Workshop on Computational Diffusion MRI
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xi, 304 p. :ill., digital ;24 cm.
內容註:	Super-Resolution Reconstruction from Accelerated Slice-Interleaved Diffusion Encoding Data -- Towards optimal sampling in diffusion MRI for accelerated fiber tractography -- A Signal Peak Separation Index for axisymmetric B-tensor encoding -- Improving tractography accuracy using dynamic filtering -- Diffeomorphic Alignment of Along-Tract Diffusion Profile Data from Tractography -- Direct reconstruction of crossing muscle fibers in the human tongue using a deep neural network -- Learning Anatomical Segmentations for Tractography from Diffusion MRI -- Diffusion MRI fiber orientation distribution function estimation using voxel-wise spherical U-net -- Stick Stippling for Joint 3D Visualization of Diffusion MRI Fiber Orientations and Density -- Q-space quantitative diffusion MRI measures using a stretched-exponential representation -- Repeatability of soma and neurite metrics in cortical and subcortical grey matter -- DW-MRI Microstructure Model of Models Captured via Single-Shell Bottleneck Deep Learning -- Deep learning model fitting for diffusion-relaxometry: a comparative study -- Pretraining Improves Deep Learning Based Tissue Microstructure Estimation -- Enhancing Diffusion Signal Augmentation using Spherical Convolutions -- Hybrid Graph Convolutional Neural Networks for Super Resolution of DW Images -- Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis -- Beyond lesion-load: Tractometry-informed metrics for characterizing white matter lesions within fibre pathways -- Multi-modal brain age estimation: a comparative study confirms the importance of microstructure -- Longitudinal Parcellation of the Infant Cortex Using Multi-Modal Connectome Harmonics -- Automatic segmentation of dentate nuclei for microstructure assessment: example of application to temporal lobe epilepsy patients -- Two Parallel Stages Deep Learning Network for Anterior Visual Pathway Segmentation -- Exploring DTI Benchmark Databases Through Visual Analytics.
Contained By:	Springer Nature eBook
標題:	Diffusion magnetic resonance imaging - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-030-73018-5
ISBN:	9783030730185

Computational diffusion MRI = International MICCAI Workshop, Lima, Peru, October 2020 /
Computational diffusion MRIInternational MICCAI Workshop, Lima, Peru, October 2020 /[electronic resource] :edited by Noemi Gyori ... [et al.]. - Cham :Springer International Publishing :2021. - xi, 304 p. :ill., digital ;24 cm. - Mathematics and visualization,2197-666X. - Mathematics and visualization..

Super-Resolution Reconstruction from Accelerated Slice-Interleaved Diffusion Encoding Data -- Towards optimal sampling in diffusion MRI for accelerated fiber tractography -- A Signal Peak Separation Index for axisymmetric B-tensor encoding -- Improving tractography accuracy using dynamic filtering -- Diffeomorphic Alignment of Along-Tract Diffusion Profile Data from Tractography -- Direct reconstruction of crossing muscle fibers in the human tongue using a deep neural network -- Learning Anatomical Segmentations for Tractography from Diffusion MRI -- Diffusion MRI fiber orientation distribution function estimation using voxel-wise spherical U-net -- Stick Stippling for Joint 3D Visualization of Diffusion MRI Fiber Orientations and Density -- Q-space quantitative diffusion MRI measures using a stretched-exponential representation -- Repeatability of soma and neurite metrics in cortical and subcortical grey matter -- DW-MRI Microstructure Model of Models Captured via Single-Shell Bottleneck Deep Learning -- Deep learning model fitting for diffusion-relaxometry: a comparative study -- Pretraining Improves Deep Learning Based Tissue Microstructure Estimation -- Enhancing Diffusion Signal Augmentation using Spherical Convolutions -- Hybrid Graph Convolutional Neural Networks for Super Resolution of DW Images -- Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis -- Beyond lesion-load: Tractometry-informed metrics for characterizing white matter lesions within fibre pathways -- Multi-modal brain age estimation: a comparative study confirms the importance of microstructure -- Longitudinal Parcellation of the Infant Cortex Using Multi-Modal Connectome Harmonics -- Automatic segmentation of dentate nuclei for microstructure assessment: example of application to temporal lobe epilepsy patients -- Two Parallel Stages Deep Learning Network for Anterior Visual Pathway Segmentation -- Exploring DTI Benchmark Databases Through Visual Analytics.

This book gathers papers presented at the Workshop on Computational Diffusion MRI, CDMRI 2020, held under the auspices of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), which took place virtually on October 8th, 2020, having originally been planned to take place in Lima, Peru. This book presents the latest developments in the highly active and rapidly growing field of diffusion MRI. While offering new perspectives on the most recent research challenges in the field, the selected articles also provide a valuable starting point for anyone interested in learning computational techniques for diffusion MRI. The book includes rigorous mathematical derivations, a large number of rich, full-colour visualizations, and clinically relevant results. As such, it is of interest to researchers and practitioners in the fields of computer science, MRI physics, and applied mathematics. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontline applications in research and clinical practice.

ISBN: 9783030730185

Standard No.: 10.1007/978-3-030-73018-5doiSubjects--Topical Terms:

3242428
Diffusion magnetic resonance imaging
--Congresses.

LC Class. No.: RC78.7.N83 / I58 2020

Dewey Class. No.: 616.07548

Computational diffusion MRI = International MICCAI Workshop, Lima, Peru, October 2020 /
LDR:04392nmm a2200337 a 4500 001 2251480
003 DE-He213
005 20210929134907.0
006 m d
007 cr nn 008maaau
008 220215s2021 sz s 0 eng d
020 $a 9783030730185 $q (electronic bk.)
020 $a 9783030730178 $q (paper)
024 7 $a 10.1007/978-3-030-73018-5 $2 doi
035 $a 978-3-030-73018-5
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.N83 $b I58 2020
072 7 $a PDE $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PDE $2 thema
082 0 4 $a 616.07548 $2 23
090 $a RC78.7.N83 $b I61 2020
111 2 $a International Workshop on Computational Diffusion MRI $d (2020 : $c Online) $3 3518526
245 1 0 $a Computational diffusion MRI $h [electronic resource] : $b International MICCAI Workshop, Lima, Peru, October 2020 / $c edited by Noemi Gyori ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xi, 304 p. : $b ill., digital ; $c 24 cm.
490 1 $a Mathematics and visualization, $x 2197-666X
505 0 $a Super-Resolution Reconstruction from Accelerated Slice-Interleaved Diffusion Encoding Data -- Towards optimal sampling in diffusion MRI for accelerated fiber tractography -- A Signal Peak Separation Index for axisymmetric B-tensor encoding -- Improving tractography accuracy using dynamic filtering -- Diffeomorphic Alignment of Along-Tract Diffusion Profile Data from Tractography -- Direct reconstruction of crossing muscle fibers in the human tongue using a deep neural network -- Learning Anatomical Segmentations for Tractography from Diffusion MRI -- Diffusion MRI fiber orientation distribution function estimation using voxel-wise spherical U-net -- Stick Stippling for Joint 3D Visualization of Diffusion MRI Fiber Orientations and Density -- Q-space quantitative diffusion MRI measures using a stretched-exponential representation -- Repeatability of soma and neurite metrics in cortical and subcortical grey matter -- DW-MRI Microstructure Model of Models Captured via Single-Shell Bottleneck Deep Learning -- Deep learning model fitting for diffusion-relaxometry: a comparative study -- Pretraining Improves Deep Learning Based Tissue Microstructure Estimation -- Enhancing Diffusion Signal Augmentation using Spherical Convolutions -- Hybrid Graph Convolutional Neural Networks for Super Resolution of DW Images -- Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis -- Beyond lesion-load: Tractometry-informed metrics for characterizing white matter lesions within fibre pathways -- Multi-modal brain age estimation: a comparative study confirms the importance of microstructure -- Longitudinal Parcellation of the Infant Cortex Using Multi-Modal Connectome Harmonics -- Automatic segmentation of dentate nuclei for microstructure assessment: example of application to temporal lobe epilepsy patients -- Two Parallel Stages Deep Learning Network for Anterior Visual Pathway Segmentation -- Exploring DTI Benchmark Databases Through Visual Analytics.
520 $a This book gathers papers presented at the Workshop on Computational Diffusion MRI, CDMRI 2020, held under the auspices of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), which took place virtually on October 8th, 2020, having originally been planned to take place in Lima, Peru. This book presents the latest developments in the highly active and rapidly growing field of diffusion MRI. While offering new perspectives on the most recent research challenges in the field, the selected articles also provide a valuable starting point for anyone interested in learning computational techniques for diffusion MRI. The book includes rigorous mathematical derivations, a large number of rich, full-colour visualizations, and clinically relevant results. As such, it is of interest to researchers and practitioners in the fields of computer science, MRI physics, and applied mathematics. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as diffusion-relaxometry and frontline applications in research and clinical practice.
650 0 $a Diffusion magnetic resonance imaging $v Congresses. $3 3242428
650 1 4 $a Mathematical and Computational Biology. $3 1566274
650 2 4 $a Mathematics of Computing. $3 891213
650 2 4 $a Mathematical Applications in Computer Science. $3 1567978
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Gyori, Noemi. $3 3518527
710 2 $a SpringerLink (Online service) $3 836513
711 2 $a International Conference on Medical Image Computing and Computer-Assisted Intervention $n (23rd : $d 2020 : $c Online) $3 3492452
773 0 $t Springer Nature eBook
830 0 $a Mathematics and visualization. $3 1568010
856 4 0 $u https://doi.org/10.1007/978-3-030-73018-5
950 $a Mathematics and Statistics (SpringerNature-11649)