Accelerated Microstructure Quantification by Q-Space Trajectory Imaging Using Machine Learning

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.