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Subspace-constrained approaches to low-rank fMRI acceleration

Mason, Harry T; Graedel, Nadine N; Miller, Karla L; Chiew, Mark; (2021) Subspace-constrained approaches to low-rank fMRI acceleration. NeuroImage , 238 , Article 118235. 10.1016/j.neuroimage.2021.118235. Green open access

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Abstract

Acceleration methods in fMRI aim to reconstruct high fidelity images from under-sampled k-space, allowing fMRI datasets to achieve higher temporal resolution, reduced physiological noise aliasing, and increased statistical degrees of freedom. While low levels of acceleration are typically part of standard fMRI protocols through parallel imaging, there exists the potential for approaches that allow much greater acceleration. One such existing approach is k-t FASTER, which exploits the inherent low-rank nature of fMRI. In this paper, we present a reformulated version of k-t FASTER which includes additional L2 constraints within a low-rank framework. We evaluated the effect of three different constraints against existing low-rank approaches to fMRI reconstruction: Tikhonov constraints, low-resolution priors, and temporal subspace smoothness. The different approaches are separately tested for robustness to under-sampling and thermal noise levels, in both retrospectively and prospectively-undersampled finger-tapping task fMRI data. Reconstruction quality is evaluated by accurate reconstruction of low-rank subspaces and activation maps. The use of L2 constraints was found to achieve consistently improved results, producing high fidelity reconstructions of statistical parameter maps at higher acceleration factors and lower SNR values than existing methods, but at a cost of longer computation time. In particular, the Tikhonov constraint proved very robust across all tested datasets, and the temporal subspace smoothness constraint provided the best reconstruction scores in the prospectively-undersampled dataset. These results demonstrate that regularized low-rank reconstruction of fMRI data can recover functional information at high acceleration factors without the use of any model-based spatial constraints.

Type: Article
Title: Subspace-constrained approaches to low-rank fMRI acceleration
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neuroimage.2021.118235
Publisher version: https://doi.org/10.1016/j.neuroimage.2021.118235
Language: English
Additional information: © 2021 The Authors. Published by Elsevier Inc. under a Creative Commons license (http://creativecommons.org/licenses/by/4.0/).
Keywords: fMRI, Acceleration, Temporal Resolution, Low Rank, k-t FASTER, Tikhonov Regularization, Temporal Smoothing, Low Resolution Priors
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 Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10178081
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