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Computational Diffusion MRI

International MICCAI Workshop, Granada, Spain, September 2018

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Elisenda Bonet-Carne

    1. Centre for Medical Image Computing, University College London, London, UK

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  2. Francesco Grussu

    1. Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK

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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. Farshid Sepehrband

    1. Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, USA

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  5. Chantal M. W. Tax

    1. Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK

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  • Contributions on new important topics that are gaining momentum within the diffusion MRI community
  • Details new computational methods and estimation techniques for microstructure imaging and brain connectivity mapping
  • Features papers presented at the 2018 MICCAI Workshop on Computational Diffusion MRI (CDMRI’18)

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

Included in the following conference series:

Conference proceedings info: MICCAI 2019.

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About this book

This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI’18), which was held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention in Granada, Spain on September 20, 2018. 


It presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find papers on 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 harmonisation and frontline applications in research and clinical practice. The respective papers constitute invited works from high-profile researchers with a specific focus on three topics that are now gaining momentum within the diffusion MRI community: i) machine learning for diffusion MRI; ii) diffusion MRI outside the brain (e.g. in the placenta); and iii) diffusion MRI for multimodal imaging. 


The book shares new perspectives on the latest research challenges for those currently working in the field, but also offers a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. It includes rigorous mathematical derivations, a wealth of 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 alike. 

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Table of contents (31 papers)

  1. Front Matter

    Pages i-xii
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  2. Diffusion MRI Signal Acquisition and Processing Strategies

    1. Front Matter

      Pages 1-1
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    2. Towards Optimal Sampling in Diffusion MRI

      • Hans Knutsson
      Pages 3-18

    3. Joint Image Reconstruction and Phase Corruption Maps Estimation in Multi-shot Echo Planar Imaging

      • Iñaki Rabanillo, Santiago Sanz-Estébanez, Santiago Aja-Fernández, Joseph Hajnal, Carlos Alberola-López, Lucilio Cordero-Grande
      Pages 19-27

    4. Return-to-Axis Probability Calculation from Single-Shell Acquisitions

      • Santiago Aja-Fernández, Antonio Tristán-Vega, Malwina Molendowska, Tomasz Pieciak, Rodrigo de Luis-García
      Pages 29-41

    5. A Novel Spatial-Angular Domain Regularisation Approach for Restoration of Diffusion MRI

      • Alessandro Mella, Alessandro Daducci, Giandomenico Orlandi, Jean-Philippe Thiran, Maria Deprez, Merixtell Bach Cuadra
      Pages 43-53

    6. Dmipy, A Diffusion Microstructure Imaging Toolbox in Python to Improve Research Reproducibility

      • Abib Alimi, Rutger Fick, Demian Wassermann, Rachid Deriche
      Pages 55-67

    7. Tissue Segmentation Using Sparse Non-negative Matrix Factorization of Spherical Mean Diffusion MRI Data

      • Peng Sun, Ye Wu, Geng Chen, Jun Wu, Dinggang Shen, Pew-Thian Yap
      Pages 69-76

    8. A Closed-Form Solution of Rotation Invariant Spherical Harmonic Features in Diffusion MRI

      • Mauro Zucchelli, Samuel Deslauriers-Gauthier, Rachid Deriche
      Pages 77-89

    9. Orientation-Dispersed Apparent Axon Diameter via Multi-Stage Spherical Mean Optimization

      • Marco Pizzolato, Demian Wassermann, Rachid Deriche, Jean-Philippe Thiran, Rutger Fick
      Pages 91-101

  3. Machine Learning for Diffusion MRI

    1. Front Matter

      Pages 103-103
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    2. Current Applications and Future Promises of Machine Learning in Diffusion MRI

      • Daniele Ravi, Nooshin Ghavami, Daniel C. Alexander, Andrada Ianus
      Pages 105-121

    3. q-Space Learning with Synthesized Training Data

      • Chuyang Ye, Yue Cui, Xiuli Li
      Pages 123-132

    4. Graph-Based Deep Learning for Prediction of Longitudinal Infant Diffusion MRI Data

      • Jaeil Kim, Yoonmi Hong, Geng Chen, Weili Lin, Pew-Thian Yap, Dinggang Shen
      Pages 133-141

    5. Supervised Classification of White Matter Fibers Based on Neighborhood Fiber Orientation Distributions Using an Ensemble of Neural Networks

      • Devran Ugurlu, Zeynep Firat, Ugur Ture, Gozde Unal
      Pages 143-154

  4. Diffusion MRI Signal Harmonization

    1. Front Matter

      Pages 155-155
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    2. Challenges and Opportunities in dMRI Data Harmonization

      • Alyssa H. Zhu, Daniel C. Moyer, Talia M. Nir, Paul M. Thompson, Neda Jahanshad
      Pages 157-172

    3. Spherical Harmonic Residual Network for Diffusion Signal Harmonization

      • Simon Koppers, Luke Bloy, Jeffrey I. Berman, Chantal M. W. Tax, J. Christopher Edgar, Dorit Merhof
      Pages 173-182

    4. Longitudinal Harmonization for Improving Tractography in Baby Diffusion MRI

      • Khoi Minh Huynh, Jaeil Kim, Geng Chen, Ye Wu, Dinggang Shen, Pew-Thian Yap
      Pages 183-191

    5. Inter-Scanner Harmonization of High Angular Resolution DW-MRI Using Null Space Deep Learning

      • Vishwesh Nath, Prasanna Parvathaneni, Colin B. Hansen, Allison E. Hainline, Camilo Bermudez, Samuel Remedios et al.
      Pages 193-201
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Other volumes

  1. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  2. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  3. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  4. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  5. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  6. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

  7. Computational Biomechanics for Medicine

  8. Computational Diffusion MRI

Editors and Affiliations

  • Centre for Medical Image Computing, University College London, London, UK

    Elisenda Bonet-Carne

  • Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK

    Francesco Grussu

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

    Lipeng Ning

  • Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, USA

    Farshid Sepehrband

  • Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK

    Chantal M. W. Tax

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