Multicompartment Magnetic Resonance Fingerprinting

TL;DR

This paper introduces a multicompartment magnetic resonance fingerprinting model that accounts for multiple tissues within a voxel, improving parameter map accuracy by using reweighted-l1 regularization for sparse recovery.

Contribution

It proposes a novel multicompartment MRF model and an efficient sparse recovery method using reweighted-l1 regularization, addressing limitations of existing single-tissue assumptions.

Findings

Validated with simulated data at various noise levels

Demonstrated effectiveness on a clinical MRI system

Improved accuracy in parameter estimation at tissue boundaries

Abstract

Magnetic resonance fingerprinting (MRF) is a technique for quantitative estimation of spin-relaxation parameters from magnetic-resonance data. Most current MRF approaches assume that only one tissue is present in each voxel, which neglects the tissue's microstructure, and may lead to artifacts in the recovered parameter maps at boundaries between tissues. In this work, we propose a multicompartment MRF model that accounts for the presence of multiple tissues per voxel. The model is fit to the data by iteratively solving a sparse linear inverse problem at each voxel, in order to express the magnetization signal as a linear combination of a few fingerprints in the precomputed dictionary. Thresholding-based methods commonly used for sparse recovery and compressed sensing do not perform well in this setting due to the high local coherence of the dictionary. Instead, we solve this challenging sparse-recovery problem by applying reweighted-l1-norm regularization, implemented using an efficient interior-point method. The proposed approach is validated with simulated data at different noise levels and undersampling factors, as well as with a controlled phantom imaging experiment on a clinical magnetic-resonance system.