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Computational Diffusion MRI

MICCAI Workshop, Québec, Canada, September 2017

  • Conference proceedings
  • © 2018

Overview

Editors:
  1. Enrico Kaden

    1. Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom

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  2. Francesco Grussu

    1. Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom

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  3. Lipeng Ning

    1. Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA

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  4. Chantal M. W. Tax

    1. Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom

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  5. Jelle Veraart

    1. Center for Biomedical Imaging, New York University School of Medicine, New York, USA

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  • Features the papers presented at the 2017 MICCAI Workshop on Computational Diffusion MRI (CDMRI’17)
  • Details new computational methods and estimation techniques for microstructure imaging and brain connectivity mapping
  • Includes rigorous mathematical derivations, full-colour visualisations and frontline clinical applications

Part of the book series: Mathematics and Visualization (MATHVISUAL)

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Table of contents (18 papers)

  1. Front Matter

    Pages i-xi
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  2. Data Acquisition and Modeling

    1. Front Matter

      Pages 1-1
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    2. Estimating Tissue Microstructure Using Diffusion-Weighted Magnetic Resonance Spectroscopy of Brain Metabolites

      • Marco Palombo
      Pages 3-19

    3. (k, q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior

      • Evan Schwab, René Vidal, Nicolas Charon
      Pages 21-35

    4. Spatio-Temporal dMRI Acquisition Design: Reducing the Number of qτ Samples Through a Relaxed Probabilistic Model

      • Patryk Filipiak, Rutger Fick, Alexandra Petiet, Mathieu Santin, Anne-Charlotte Philippe, Stephane Lehericy et al.
      Pages 37-49

    5. A Generalized SMT-Based Framework for Diffusion MRI Microstructural Model Estimation

      • Mauro Zucchelli, Maxime Descoteaux, Gloria Menegaz
      Pages 51-63

  3. Image Postprocessing

    1. Front Matter

      Pages 65-65
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    2. Diffusion Specific Segmentation: Skull Stripping with Diffusion MRI Data Alone

      • Robert I. Reid, Zuzana Nedelska, Christopher G. Schwarz, Chadwick Ward, Clifford R. Jack Jr., The Alzheimer’s Disease Neuroimaging Initiative
      Pages 67-80

    3. Diffeomorphic Registration of Diffusion Mean Apparent Propagator Fields Using Dynamic Programming on a Minimum Spanning Tree

      • Kévin Ginsburger, Fabrice Poupon, Achille Teillac, Jean-Francois Mangin, Cyril Poupon
      Pages 81-90

    4. Diffusion Orientation Histograms (DOH) for Diffusion Weighted Image Analysis

      • Laurent Chauvin, Kuldeep Kumar, Christian Desrosiers, Jacques De Guise, Matthew Toews
      Pages 91-99

  4. Tractography and Connectivity

    1. Front Matter

      Pages 101-101
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    2. Learning a Single Step of Streamline Tractography Based on Neural Networks

      • Daniel Jörgens, Örjan Smedby, Rodrigo Moreno
      Pages 103-116

    3. Probabilistic Tractography for Complex Fiber Orientations with Automatic Model Selection

      • Edwin Versteeg, Frans M. Vos, Gert Kwakkel, Frans C. T. van der Helm, Joor A. M. Arkesteijn, Olena Filatova
      Pages 117-128

    4. Bundle-Specific Tractography

      • Francois Rheault, Etienne St-Onge, Jasmeen Sidhu, Quentin Chenot, Laurent Petit, Maxime Descoteaux
      Pages 129-139

    5. A Sheet Probability Index from Diffusion Tensor Imaging

      • Michael Ankele, Thomas Schultz
      Pages 141-154

    6. Recovering Missing Connections in Diffusion Weighted MRI Using Matrix Completion

      • Chendi Wang, Bernard Ng, Alborz Amir-Khalili, Rafeef Abugharbieh
      Pages 155-164

    7. Brain Parcellation and Connectivity Mapping Using Wasserstein Geometry

      • Hamza Farooq, Yongxin Chen, Tryphon Georgiou, Christophe Lenglet
      Pages 165-174

    8. Exploiting Machine Learning Principles for Assessing the Fingerprinting Potential of Connectivity Features

      • Silvia Obertino, Sofía Jiménez Hernández, Ilaria Boscolo Galazzo, Francesca Benedetta Pizzini, Mauro Zucchelli, Gloria Menegaz
      Pages 175-188

  5. Clinical Applications

    1. Front Matter

      Pages 189-189
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    2. Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players

      • Itay Benou, Ronel Veksler, Alon Friedman, Tammy Riklin Raviv
      Pages 191-204
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Keywords

About this book

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.

Editors and Affiliations

  • Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom

    Enrico Kaden

  • Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom

    Francesco Grussu

  • Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA

    Lipeng Ning

  • Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom

    Chantal M. W. Tax

  • Center for Biomedical Imaging, New York University School of Medicine, New York, USA

    Jelle Veraart

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