Enhancing the estimation of fiber orientation distributions using convolutional neural networks

TY - JOUR

T1 - Enhancing the estimation of fiber orientation distributions using convolutional neural networks

AU - Lucena, Oeslle

AU - Vos, Sjoerd B.

AU - Vakharia, Vejay

AU - Duncan, John

AU - Ashkan, Keyoumars

AU - Sparks, Rachel

AU - Ourselin, Sebastien

N1 - Funding Information: This research was funded by the National Institute for Health Research ( NIHR ) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London and the NIHR Clinical Research Facility. Oeslle Lucena is funded by EPSRC Research Council ( EPSRC DTP EP/R513064/1). Sjoerd B. Vos is funded by the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative BW.mn.BRC10269). We also thank NVIDIA for providing the Titan V GPU used in this work. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Publisher Copyright: © 2021 The Author(s) Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/8

Y1 - 2021/8

N2 - Local fiber orientation distributions (FODs) can be computed from diffusion magnetic resonance imaging (dMRI). The accuracy and ability of FODs to resolve complex fiber configurations benefits from acquisition protocols that sample a high number of gradient directions, a high maximum b-value, and multiple b-values. However, acquisition time and scanners that follow these standards are limited in clinical settings, often resulting in dMRI acquired at a single shell (single b-value). In this work, we learn improved FODs from clinically acquired dMRI. We evaluate patch-based 3D convolutional neural networks (CNNs) on their ability to regress multi-shell FODs from single-shell FODs, using constrained spherical deconvolution (CSD). We evaluate U-Net and High-Resolution Network (HighResNet) 3D CNN architectures on data from the Human Connectome Project and an in-house dataset. We evaluate how well each CNN can resolve FODs 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN. This work is a step towards more accurate FOD estimation in time- and resource-limited clinical environments.

AB - Local fiber orientation distributions (FODs) can be computed from diffusion magnetic resonance imaging (dMRI). The accuracy and ability of FODs to resolve complex fiber configurations benefits from acquisition protocols that sample a high number of gradient directions, a high maximum b-value, and multiple b-values. However, acquisition time and scanners that follow these standards are limited in clinical settings, often resulting in dMRI acquired at a single shell (single b-value). In this work, we learn improved FODs from clinically acquired dMRI. We evaluate patch-based 3D convolutional neural networks (CNNs) on their ability to regress multi-shell FODs from single-shell FODs, using constrained spherical deconvolution (CSD). We evaluate U-Net and High-Resolution Network (HighResNet) 3D CNN architectures on data from the Human Connectome Project and an in-house dataset. We evaluate how well each CNN can resolve FODs 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN. This work is a step towards more accurate FOD estimation in time- and resource-limited clinical environments.

KW - Constrained spherical deconvolution

KW - Deep learning

KW - Diffusion weighted image

KW - Tractography

UR - http://www.scopus.com/inward/record.url?scp=85110501386&partnerID=8YFLogxK

U2 - 10.1016/j.compbiomed.2021.104643

DO - 10.1016/j.compbiomed.2021.104643

M3 - Article

C2 - 34280774

AN - SCOPUS:85110501386

SN - 0010-4825

VL - 135

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

M1 - 104643

ER -