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Computational diffusion MRI : MICCAI Worskhop, Québec, Canada, September 2017

Author: Enrico Kaden; Francesco Grusso; Lipeng Ning; Chantal M W Tax; Jelle Veraart
Publisher: Cham : Springer, [2018] ©2018
Series: Mathematics and visualization.
Edition/Format:   eBook : Document : Conference publication : EnglishView all editions and formats
Summary:
This volume presents the latest developments in the highly active and rapidly growing field of diffusion MRI. 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 frontline  Read more...
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Genre/Form: Conference papers and proceedings
Electronic books
Congresses
Additional Physical Format: Print version:
(OCoLC)1016971791
Material Type: Conference publication, Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Enrico Kaden; Francesco Grusso; Lipeng Ning; Chantal M W Tax; Jelle Veraart
ISBN: 9783319738390 3319738399
OCLC Number: 1030486236
Description: 1 online resource : illustrations
Contents: Intro; Preface; Program Committee; Contents; Part I Data Acquisition and Modeling; Estimating Tissue Microstructure Using Diffusion-Weighted Magnetic Resonance Spectroscopy of Brain Metabolites; 1 Introduction; 2 MRS and DW-MRS (Very) Briefly; 3 Geometrical Models for Tissue Microstructure Estimates Using the Diffusion of Brain Metabolites; 4 Computational Models for Cell Morphology Estimates Using the Diffusion of Brain Metabolites; 5 Conclusion; References; (k, q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior; 1 Introduction; 2 Background and Prior Work; 3 Methods. Part II Image PostprocessingDiffusion Specific Segmentation: Skull Stripping with Diffusion MRI Data Alone; 1 Introduction; 1.1 Desired Properties of a dMRI Segmenter; 2 Methods; 2.1 Feature Extraction; 2.2 Classifier Selection; 2.3 Morphological Filtering; 2.4 Comparison with Other Intracranial Mask Generation Methods; 3 Data, Training, and Testing; 3.1 Data; 3.2 Training; 3.3 Testing; 4 Results; 5 Conclusions; References; Diffeomorphic Registration of Diffusion Mean Apparent Propagator Fields Using Dynamic Programming on a Minimum Spanning Tree; 1 Introduction; 2 Methods. 2.1 Discrete Representation of MAPs and Reorientation2.2 MAP Similarity Measure; 2.3 Diffeomorphic Registration of MAP Fields; 3 Results and Discussion; 4 Conclusion; References; Diffusion Orientation Histograms (DOH) for Diffusion Weighted Image Analysis; 1 Introduction; 2 Related Work; 3 Diffusion Orientation Histogram Descriptors; 4 Experiments; 5 Discussion; References; Part III Tractography and Connectivity; Learning a Single Step of Streamline Tractography Based on Neural Networks; 1 Introduction; 2 Methods; 2.1 Input to the Network; 2.2 Output of the Network and Post Processing.
Series Title: Mathematics and visualization.
Responsibility: Enrico Kaden, Francesco Grusso, Lipeng Ning, Chantal M.W. Tax, Jelle Veraart, editors.

Abstract:

This volume presents the latest developments in the highly active and rapidly growing field of diffusion MRI. 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 frontline applications in neuroscience research and clinical practice. These proceedings contain the papers presented at the 2017 MICCAI Workshop on Computational Diffusion MRI (CDMRI'17) held in Québec, Canada on September 10, 2017, sharing new perspectives on the most recent research challenges for those currently working in the field, but also offering a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. This book includes rigorous mathematical derivations, a large number of rich, full-colour visualisations and clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics.

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