Keywords: Microstructure, Machine Learning/Artificial Intelligence, Q-space, QTI, Microscopic Anisotropy, µFA
Motivation: Tensor-encoded diffusion MRI (dMRI) methods for tissue microstructure elucidation typically require lengthy dMRI acquisitions and computationally costly, SNR-sensitive data analysis.
Goal(s): Employing q-space trajectory imaging (QTI), we seek to greatly reduce both the number of required measurements and computational burden in analysis for robust estimation of parameters quantifying brain tissue microstructure.
Approach: A machine learning-based estimator is trained on a 10-fold reduced subset of an extensive dMRI protocol acquired in 18 healthy volunteers.
Results: The proposed method outperforms a state-of-the-art model fitting framework, yielding smoother parameter maps and showing lower deviation from the chosen ground truth, even at reduced SNR/increased resolution.
Impact: Quantitative measures of brain microstructure are obtained by accelerated tensor-encoded diffusion MRI, employing a voxel-wise regression neural network. Observed resilience at reduced voxel size (1.7mm)3 appears promising regarding measurement of parameters such as microscopic fractional anisotropy in a clinical setting.
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