Contrast-optimized basis functions for self-navigated motion correction in quantitative MRI

The long scan times of quantitative MRI techniques make them vulnerable to motion artifacts. For MR-Fingerprinting-like approaches, this problem can be addressed with self-navigated retrospective motion correction based on reconstructions in a singular value decomposition (SVD) subspace. However, the SVD promotes high signal intensity in all tissues, which limits the contrast between tissue types and ultimately reduces the accuracy of registration. The purpose of this paper is to rotate the subspace for maximum contrast between two types of tissue and improve the accuracy of motion estimates.

A subspace is derived that promotes contrasts between brain parenchyma and CSF, achieved through the generalized eigendecomposition of mean autocorrelation matrices, followed by a Gram-Schmidt process to maintain orthogonality. We tested our motion correction method on 85 scans with varying motion levels, acquired with a 3D hybrid-state sequence optimized for quantitative magnetization transfer imaging.

A comparative analysis shows that the contrast-optimized basis significantly improves the parenchyma-CSF contrast, leading to more accurate motion estimates and reduced artifacts in the quantitative maps.

The proposed contrast-optimized subspace improves the accuracy of the motion estimation.