Contrast-Optimized Basis Functions for Self-Navigated Motion Correction in Quantitative MRI

TL;DR

This paper introduces a contrast-optimized subspace for self-navigated motion correction in quantitative MRI, enhancing tissue contrast and improving motion estimate accuracy in scans affected by motion artifacts.

Contribution

It develops a novel subspace rotation method using generalized eigendecomposition to maximize tissue contrast for better motion correction in MRI.

Findings

Enhanced tissue contrast between brain parenchyma and CSF

Smoother motion estimates in MRI scans

Reduced artifacts in quantitative maps

Abstract

Purpose: The long scan times of quantitative MRI techniques make motion artifacts more likely. 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. Methods: 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. Results: A comparative analysis shows that the contrast-optimized basis significantly improve the parenchyma-CSF contrast, leading to smoother motion estimates and reduced artifacts in the quantitative maps. Conclusion: The proposed contrast-optimized subspace improves the accuracy of the motion estimation.