Assessing the feasibility of estimating axon diameter using diffusion models and machine learning

Rutger H.J. Fick, Neda Sepasian, Marco Pizzolato, Andrada Ianus, Rachid Deriche

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

Axon diameter estimation has been a focus of the diffusion MRI community for the past decade. The main argument has been that while diffusion models always overestimate the true axon diameter, their estimation still correlates with changes in true value. Until now, this remains more as a discussion point. The aim of this paper is to clarify this hypothesis using a recently acquired cat spinal cord data set, where the diffusion MRI signal of both a multi-shell and Ax-Caliber acquisition have been registered with the underlying histology values. We find that the axon diameter as estimated by signal models and AxCaliber does not correlate with their true sizes for axon diameters smaller than 3 μm. On the other hand, we also train a random forest machine learning algorithm to map signal-based features to histology values of axon diameter and volume fraction. The results show that, in this dataset, this approach leads to a more reliable estimation of physically relevant axon diameters than using sophisticated diffusion models.

Original languageEnglish
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
Place of PublicationPiscataway
PublisherIEEE Computer Society
Pages766-769
Number of pages4
ISBN (Electronic)978-1-5090-1172-8
ISBN (Print)978-1-5090-1173-5
DOIs
Publication statusPublished - 15 Jun 2017
Externally publishedYes
Event14th IEEE International Symposium on Biomedical Imaging (ISBI 2017) - Melbourne, Australia
Duration: 18 Apr 201721 Apr 2017

Conference

Conference14th IEEE International Symposium on Biomedical Imaging (ISBI 2017)
Country/TerritoryAustralia
CityMelbourne
Period18/04/1721/04/17

Keywords

  • 1D-SHORE
  • AxCaliber
  • Axon Diameter
  • Diffusion MRI
  • Histology
  • Machine Learning
  • MAP-MRI
  • Random Forest
  • Validation

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