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E-grāmata: Computational Diffusion MRI: International MICCAI Workshop, Lima, Peru, October 2020

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  • Formāts: PDF+DRM
  • Sērija : Mathematics and Visualization
  • Izdošanas datums: 29-Sep-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • Valoda: eng
  • ISBN-13: 9783030730185
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Computational Diffusion MRI: International MICCAI Workshop, Lima, Peru, October 2020Palielināt
  • Formāts: PDF+DRM
  • Sērija : Mathematics and Visualization
  • Izdošanas datums: 29-Sep-2021
  • Izdevniecība: Springer Nature Switzerland AG
  • Valoda: eng
  • ISBN-13: 9783030730185
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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.


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.
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Permanent link: https://www.kriso.lv/db/97830307301852e.html
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