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Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data

Kerkelä, Leevi; Seunarine, Kiran; Szczepankiewicz, Filip; Clark, Chris A; (2024) Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data. Frontiers in Neuroimaging , 3 , Article 1349415. 10.3389/fnimg.2024.1349415. Green open access

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Abstract

Diffusion magnetic resonance imaging is sensitive to the microstructural properties of brain tissue. However, estimating clinically and scientifically relevant microstructural properties from the measured signals remains a highly challenging inverse problem that machine learning may help solve. This study investigated if recently developed rotationally invariant spherical convolutional neural networks can improve microstructural parameter estimation. We trained a spherical convolutional neural network to predict the ground-truth parameter values from efficiently simulated noisy data and applied the trained network to imaging data acquired in a clinical setting to generate microstructural parameter maps. Our network performed better than the spherical mean technique and multi-layer perceptron, achieving higher prediction accuracy than the spherical mean technique with less rotational variance than the multi-layer perceptron. Although we focused on a constrained two-compartment model of neuronal tissue, the network and training pipeline are generalizable and can be used to estimate the parameters of any Gaussian compartment model. To highlight this, we also trained the network to predict the parameters of a three-compartment model that enables the estimation of apparent neural soma density using tensor-valued diffusion encoding.

Type: Article
Title: Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fnimg.2024.1349415
Publisher version: http://dx.doi.org/10.3389/fnimg.2024.1349415
Language: English
Additional information: © 2024 Kerkelä, Seunarine, Szczepankiewicz and Clark. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: diffusion magnetic resonance imaging, geometric deep learning, microstructure, spherical convolutional neural network, MRI
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Neurosciences Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10189221
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