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Computational Diffusion MRI

MICCAI Workshop, Athens, Greece, October 2016

  • Conference proceedings
  • © 2017

Overview

Editors:
  1. Andrea Fuster

    1. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

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  2. Aurobrata Ghosh

    1. Centre for Medical Image Computing, University College London, London, United Kingdom

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  3. Enrico Kaden

    1. Centre for Medical Image Computing, University College London, London, United Kingdom

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  4. Yogesh Rathi

    1. Department of Psychiatry, Brigham & Womens Hospital, Boston, USA

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    You can also search for this editor in PubMed   Google Scholar

  5. Marco Reisert

    1. Medical Physics, University Medical Center, Freiburg, Germany

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  • Careful mathematical derivations
  • Large number of rich full-color visualizations
  • Biologically or clinically relevant results

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

Included in the following conference series:

Conference proceedings info: MICCAI 2016.

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Table of contents (17 papers)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. The MR Physics of Advanced Diffusion Imaging

    • Matt G. Hall
    Pages 1-20

  3. Noise Floor Removal via Phase Correction of Complex Diffusion-Weighted Images: Influence on DTI and q-Space Metrics

    • Marco Pizzolato, Rutger Fick, Timothé Boutelier, Rachid Deriche
    Pages 21-34

  4. Regularized Dictionary Learning with Robust Sparsity Fitting for Compressed Sensing Multishell HARDI

    • Kratika Gupta, Deepali Adlakha, Vishal Agarwal, Suyash P. Awate
    Pages 35-48

  5. Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets

    • Jian Zhang, Geng Chen, Yong Zhang, Bin Dong, Dinggang Shen, Pew-Thian Yap
    Pages 49-59

  6. Diffusion MRI Signal Augmentation: From Single Shell to Multi Shell with Deep Learning

    • Simon Koppers, Christoph Haarburger, Dorit Merhof
    Pages 61-70

  7. Multi-Spherical Diffusion MRI: Exploring Diffusion Time Using Signal Sparsity

    • Rutger H. J. Fick, Alexandra Petiet, Mathieu Santin, Anne-Charlotte Philippe, Stephane Lehericy, Rachid Deriche et al.
    Pages 71-83

  8. Sensitivity of OGSE ActiveAx to Microstructural Dimensions on a Clinical Scanner

    • Lebina S. Kakkar, David Atkinson, Rachel W. Chan, Bernard Siow, Andrada Ianus, Ivana Drobnjak
    Pages 85-97

  9. Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models

    • Guillermo Gallardo, Rutger Fick, William Wells III, Rachid Deriche, Demian Wassermann
    Pages 99-112

  10. Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion

    • Zhanlong Yang, Geng Chen, Dinggang Shen, Pew-Thian Yap
    Pages 113-121

  11. Parcellation of Human Amygdala Subfields Using Orientation Distribution Function and Spectral K-means Clustering

    • Qiuting Wen, Brian D. Stirling, Long Sha, Li Shen, Paul J. Whalen, Yu-Chien Wu
    Pages 123-132

  12. Sparse Representation for White Matter Fiber Compression and Calculation of Inter-Fiber Similarity

    • Gali Zimmerman Moreno, Guy Alexandroni, Nir Sochen, Hayit Greenspan
    Pages 133-143

  13. An Unsupervised Group Average Cortical Parcellation Using Diffusion MRI to Probe Cytoarchitecture

    • Tara Ganepola, Zoltan Nagy, Daniel C. Alexander, Martin I. Sereno
    Pages 145-156

  14. Using Multiple Diffusion MRI Measures to Predict Alzheimer’s Disease with a TV-L1 Prior

    • Julio E. Villalon-Reina, Talia M. Nir, Boris A. Gutman, Neda Jahanshad, Clifford R. Jack Jr, Michael W. Weiner et al.
    Pages 157-166

  15. Accurate Diagnosis of SWEDD vs. Parkinson Using Microstructural Changes of Cingulum Bundle: Track-Specific Analysis

    • Farzaneh Rahmani, Somayeh Mohammadi Jooyandeh, Mohammad Hadi Shadmehr, Ahmad Shojaie, Farsad Noorizadeh, Mohammad Hadi Aarabi
    Pages 167-173

  16. Colocalization of Functional Activity and Neurite Density Within Cortical Areas

    • Achille Teillac, Sandrine Lefrance, Edouard Duchesnay, Fabrice Poupon, Maite Alaitz Ripoll Fuster, Denis Le Bihan et al.
    Pages 175-186

  17. Comparison of Biomarkers in Transgenic Alzheimer Rats Using Multi-Shell Diffusion MRI

    • Rutger H. J. Fick, Madelaine Daianu, Marco Pizzolato, Demian Wassermann, Russell E. Jacobs, Paul M. Thompson et al.
    Pages 187-199

  18. Working Memory Function in Recent-Onset Schizophrenia Patients Associated with White Matter Microstructure: Connectometry Approach

    • Mahsa Dolatshahi, Farzaneh Rahmani, Mohammad Hadi Shadmehr, Timm Peoppl, Ahmad Shojaie, Farsad Noorizadeh et al.
    Pages 201-209

  19. Back Matter

    Pages 211-212
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Other volumes

  1. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

  2. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

  3. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016

  4. Computational Diffusion MRI

Keywords

About this book

This volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity, while also sharing new perspectives and insights on the latest research challenges for those currently working in the field.

Over the last decade, interest in diffusion MRI has virtually exploded. The technique provides unique insights into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into the clinic, while new processing methods are essential to addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference.

These papers from the 2016 MICCAI Workshop “Computational Diffusion MRI” – which was intended to provide a snapshot of the latest developments within the highly active and growing field of diffusion MR – cover a wide range of topics, from fundamental theoretical work on mathematical modeling, to the development and evaluation of robust algorithms and applications in neuroscientific studies and clinical practice. The contributions include rigorous mathematical derivations, a wealth of rich, full-color visualizations, and biologically or clinically relevant results. As such, they will be of interest to researchers and practitioners in the fields of computer science, MR physics, and applied mathematics. 

Editors and Affiliations

  • Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Andrea Fuster

  • Centre for Medical Image Computing, University College London, London, United Kingdom

    Aurobrata Ghosh, Enrico Kaden

  • Department of Psychiatry, Brigham & Womens Hospital, Boston, USA

    Yogesh Rathi

  • Medical Physics, University Medical Center, Freiburg, Germany

    Marco Reisert

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