Contrast-Optimized Basis Functions for Self-Navigated Motion Correction in 3D quantitative MRI

Motivation: Motion-induced artifacts are a significant barrier to achieving clinically acceptable image quality for multi-compartment quantitative MRI techniques, e.g. a 2-pool magnetization transfer model. Goal(s): To develop a self-navigating approach to estimating motion parameters in an MR-fingerprinting-like acquisition. Approach: We optimize a subspace that maximizes the contrast-to-noise ratio between brain parenchyma and cerebrospinal fluid for a low-resolution, time-segmented low-rank reconstruction used to estimate motion. Results: Compared to the typical SVD basis, the contrast-optimized basis improves the smoothness of the motion estimates and the apparent resolution of the reconstructed coefficient images and quantitative maps. Impact: The proposed retrospective, self-navigating motion correction technique does not require any sequence modifications and/or additional scan time. It can therefore be applied to many quantitative MRI techniques where the signal's variation over time can be well-described in a low-rank subspace.