Computational Diffusion MRI

1. Front Matter

   Pages i-xi

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2. Diffusion MRI Signal Acquisition and Processing Strategies

   1. Front Matter

      Pages 1-1

      PDF

   2. Connectome 2.0: Cutting-Edge Hardware Ushers in New Opportunities for Computational Diffusion MRI

      • Anastasia Yendiki, Thomas Witzel, Susie Y. Huang

      Pages 3-12

   3. Alternative Diffusion Anisotropy Metric from Reduced MRI Acquisitions

      • Santiago Aja-Fernández, Antonio Tristán-Vega, Rodrigo de Luis-García, Derek K. Jones

      Pages 13-24

   4. Optimized Response Function Estimation for Spherical Deconvolution

      • Tom Dela Haije, Aasa Feragen

      Pages 25-34

   5. Optimal Fiber Diffusion Model Restoration

      • Clint Greene, Kate Revill, Cathrin Buetefisch, Ken Rose, Scott Grafton

      Pages 35-47

   6. Diffusion Anisotropy Identification by Short Diffusion-Diffusion Correlation Spectroscopy

      • Fangrong Zong, Yan Zhuo, Natasha Spindler, Huabing Liu, Petrik Galvosas

      Pages 49-59

3. Machine Learning for Diffusion MRI

   1. Front Matter

      Pages 61-61

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   2. Current Challenges and Future Directions in Diffusion MRI: From Model- to Data- Driven Analysis

      • Kurt G. Schilling, Baxter Rogers, Adam W. Anderson, Bennett A. Landman

      Pages 63-78

   3. Spatial Sparse Estimation of Fiber Orientation Distribution Using Deep Alternating Directions Method of Multipliers Network

      • Ridho Akbar, Yuanjing Feng, Fan Zhang, Jianzhong He, Qingrun Zeng, Lipeng Ning et al.

      Pages 79-89

   4. Free-Water Correction in Diffusion MRI: A Reliable and Robust Learning Approach

      • Leon Weninger, Simon Koppers, Chuh-Hyoun Na, Kerstin Juetten, Dorit Merhof

      Pages 91-99

   5. Convolutional Neural Networks for Fiber Orientation Distribution Enhancement to Improve Single-Shell Diffusion MRI Tractography

      • Oeslle Lucena, Sjoerd B. Vos, Vejay Vakharia, John Duncan, Sebastien Ourselin, Rachel Sparks

      Pages 101-112

   6. q-Space Novelty Detection with Variational Autoencoders

      • Aleksei Vasilev, Vladimir Golkov, Marc Meissner, Ilona Lipp, Eleonora Sgarlata, Valentina Tomassini et al.

      Pages 113-124

   7. DWI Simulation-Assisted Machine Learning Models for Microstructure Estimation

      • Jonathan Rafael-Patino, Thomas Yu, Victor Delvigne, Muhamed Barakovic, Marco Pizzolato, Gabriel Girard et al.

      Pages 125-134

   8. Convolutional Neural Network on DTI Data for Sub-cortical Brain Structure Segmentation

      • G. R. Pinheiro, D. S. Carmo, C. Yasuda, R. A. Lotufo, L. Rittner

      Pages 135-146

4. Diffusion MRI Outside the Brain and Clinical Applications

   1. Front Matter

      Pages 147-147

      PDF

   2. A Framework to Construct a Longitudinal DW-MRI Infant Atlas Based on Mixed Effects Modeling of dODF Coefficients

      • Heejong Kim, Martin Styner, Joseph Piven, Guido Gerig

      Pages 149-159

   3. Investigation of Changes in Anomalous Diffusion Parameters in a Mouse Model of Brain Tumour

      • Qianqian Yang, Simon Puttick, Zara C. Bruce, Bryan W. Day, Viktor Vegh

      Pages 161-172

   4. A Network-Based Analysis of the Preterm Adolescent Brain Using PCA and Graph Theory

      • Hassna Irzan, Michael Hütel, Carla Semedo, Helen O’Reilly, Manisha Sahota, Sebastien Ourselin et al.

      Pages 173-181

   5. Diffusion MRI Fiber Tractography by Flow Field Formation with Extended Physarum Solver: A Pilot Study with 2D Phantoms

      • Yoshitaka Masutani

      Pages 183-192

This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI 2019), held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), which took place in Shenzhen, China on October 17, 2019.

This book presents the latest advances in the rapidly expanding field of diffusion MRI. It 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 about computational techniques in diffusion MRI. The book includes rigorous mathematical derivations, a wealth of rich, full-colour visualisations and extensive 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. Readers will find 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. This edition includes invited works from high-profile researchers with a specific focus on three new and important topics that are gaining momentum within the diffusion MRI community, including diffusion MRI signal acquisition and processing strategies, machine learning for diffusion MRI, and diffusion MRI outside the brain and clinical applications.

• diffusion MRI
• multidimensional diffusion MRI
• combined diffusion-relaxometry MRI
• computational techniques
• medical image computing
• medical image analysis
• medical visualisation
• image and signal acquisition
• image registration
• image reconstruction
• image analysis
• image and signal processing
• image and signal modelling
• machine learning
• brain MRI
• neuroimaging
• connectomics
• fibre tractography
• body MRI
• microstructure imaging

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

  Elisenda Bonet-Carne

• Centre for Medical Engineering, King’s College London, London, UK

  Jana Hutter

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

  Marco Palombo

• Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

  Marco Pizzolato

• Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, USA

  Farshid Sepehrband

• Laboratory of Mathematics in Imaging, Harvard Medical School, Boston, USA

  Fan Zhang

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