Accelerating Quantitative MRI using Subspace Multiscale Energy Model (SS-MuSE)

TL;DR

This paper introduces a novel subspace multiscale energy model (SS-MuSE) to accelerate 3D multi-contrast MRI scans, reducing computation time and memory demands compared to existing deep learning methods.

Contribution

It generalizes the MuSE framework to a subspace recovery setting, enabling efficient 3D MRI reconstruction with explicit energy-based regularization.

Findings

Efficient recovery of multi-contrast 3D MRI images

Reduced computational and memory requirements

Effective joint regularization of spatial factors

Abstract

Multi-contrast MRI methods acquire multiple images with different contrast weightings, which are used for the differentiation of the tissue types or quantitative mapping. However, the scan time needed to acquire multiple contrasts is prohibitively long for 3D acquisition schemes, which can offer isotropic image resolution. While deep learning-based methods have been extensively used to accelerate 2D and 2D + time problems, the high memory demand, computation time, and need for large training data sets make them challenging for large-scale volumes. To address these challenges, we generalize the plug-and-play multi-scale energy-based model (MuSE) to a regularized subspace recovery setting, where we jointly regularize the 3D multi-contrast spatial factors in a subspace formulation. The explicit energy-based formulation allows us to use variable splitting optimization methods for computationally efficient recovery.