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Assessing the Feasibility of Estimating Axon Diameter using Diffusion Models and Machine Learning

Fick, RHJ; Sepasian, N; Pizzolato, M; Ianus, A; Deriche, R; (2017) Assessing the Feasibility of Estimating Axon Diameter using Diffusion Models and Machine Learning. In: Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). (pp. pp. 766-769). IEEE: Melbourne, VIC, Australia. Green open access

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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.

Type: Proceedings paper
Title: Assessing the Feasibility of Estimating Axon Diameter using Diffusion Models and Machine Learning
Event: IEEE 14th International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro
Location: Melbourne, AUSTRALIA
Dates: 18 April 2017 - 21 April 2017
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ISBI.2017.7950631
Publisher version: https://doi.org/10.1109/ISBI.2017.7950631
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Axons, Diffusion tensor imaging, Correlation, Radio frequency, Estimation, Spinal cord
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10061676
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