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Computational Diffusion MRI

14th International Workshop, CDMRI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Muge Karaman

    1. University of Illinois at Chicago, Chicago, USA

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  2. Remika Mito

    1. Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia

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  3. Elizabeth Powell

    1. University College London, London, UK

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  4. Francois Rheault

    1. Université de Sherbrooke, Sherbrooke, Canada

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  5. Stefan Winzeck

    1. Microsoft Research Cambridge, Milton, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14328)

Included in the following conference series:

Conference proceedings info: CDMRI 2023.

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About this book

This book constitutes the proceedings of the 14th International Workshop, CDMRI 2023, held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Vancouver, BC, Canada, on October 8, 2023.

The 17regular papers presented in this book were carefully reviewed and selected from 19 submissions. These contributions cover various aspects, including preprocessing, signal modeling, tractography, bundle segmentation, and clinical applications. Many of these studies employ novel machine learning implementations, highlighting the evolving landscape of techniques beyond the more traditional physics-based algorithms.


Keywords

Table of contents (17 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Neural Spherical Harmonics for Structurally Coherent Continuous Representation of Diffusion MRI Signal

    • Tom Hendriks, Anna Vilanova, Maxime Chamberland
    Pages 1-12

  3. A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-Weighted MRI

    • Tianyuan Yao, Nancy Newlin, Praitayini Kanakaraj, Vishwesh Nath, Leon Y. Cai, Karthik Ramadass et al.
    Pages 13-22

  4. Diffusion Phantom Study of Fiber Crossings at Varied Angles Reconstructed with ODF-Fingerprinting

    • Patryk Filipiak, Timothy M. Shepherd, Lee Basler, Anthony Zuccolotto, Dimitris G. Placantonakis, Walter Schneider et al.
    Pages 23-34

  5. Improving Multi-Tensor Fitting with Global Information from Track Orientation Density Imaging

    • Erick Hernandez-Gutierrez, Ricardo Coronado-Leija, Alonso Ramirez-Manzanares, Muhamed Barakovic, Stefano Magon, Maxime Descoteaux
    Pages 35-46

  6. BundleSeg: A Versatile, Reliable and Reproducible Approach to White Matter Bundle Segmentation

    • Etienne St-Onge, Kurt G Schilling, Francois Rheault
    Pages 47-57

  7. Automated Mapping of Residual Distortion Severity in Diffusion MRI

    • Shuo Huang, Lujia Zhong, Yonggang Shi
    Pages 58-69

  8. Automatic Fast and Reliable Recognition of a Small Brain White Matter Bundle

    • John Kruper, Ariel Rokem
    Pages 70-79

  9. Self Supervised Denoising Diffusion Probabilistic Models for Abdominal DW-MRI

    • Serge Vasylechko, Onur Afacan, Sila Kurugol
    Pages 80-91

  10. Voxlines: Streamline Transparency Through Voxelization and View-Dependent Line Orders

    • Besm Osman, Mestiez Pereira, Huub van de Wetering, Maxime Chamberland
    Pages 92-103

  11. Subnet Communicability: Diffusive Communication Across the Brain Through a Backbone Subnetwork

    • S. Shailja, Jonathan Parlett, Abhishek Jeyapratap, Ali Shokoufandeh, Birkan Tunc, Yusuf Osmanlioglu
    Pages 104-117

  12. Fast Acquisition for Diffusion Tensor Tractography

    • Omri Leshem, Nahum Kiryati, Michael Green, Ilya Nelkenbaum, Dani Roizen, Arnaldo Mayer
    Pages 118-128

  13. FASSt: Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography

    • Yuan Li, Xinyu Nie, Yao Fu, Yonggang Shi
    Pages 129-139

  14. Anisotropic Fanning Aware Low-Rank Tensor Approximation Based Tractography

    • Johannes Gruen, Jonah Sieg, Thomas Schultz
    Pages 140-151

  15. BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data

    • Yixue Feng, Bramsh Q. Chandio, Julio E. Villalón-Reina, Sophia I. Thomopoulos, Himanshu Joshi, Gauthami Nair et al.
    Pages 152-164

  16. A Deep Network for Explainable Prediction of Non-imaging Phenotypes Using Anatomical Multi-view Data

    • Yuxiang Wei, Yuqian Chen, Tengfei Xue, Leo Zekelman, Nikos Makris, Yogesh Rathi et al.
    Pages 165-176

  17. ReTrace: Topological Evaluation of White Matter Tractography Algorithms Using Reeb Graphs

    • S. Shailja, Jefferson W. Chen, Scott T. Grafton, B. S. Manjunath
    Pages 177-191

  18. Advanced Diffusion MRI Modeling Sheds Light on FLAIR White Matter Hyperintensities in an Aging Cohort

    • Kelly Chang, Luke Burke, Nina LaPiana, Bradley Howlett, David Hunt, Margaret Dezelar et al.
    Pages 192-203

  19. Back Matter

    Pages 205-206
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Other volumes

  1. Computational Diffusion MRI

Editors and Affiliations

  • University of Illinois at Chicago, Chicago, USA

    Muge Karaman

  • Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia

    Remika Mito

  • University College London, London, UK

    Elizabeth Powell

  • Université de Sherbrooke, Sherbrooke, Canada

    Francois Rheault

  • Microsoft Research Cambridge, Milton, UK

    Stefan Winzeck

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