Magnetic Resonance Fingerprinting with compressed sensing and distance metric learning

TL;DR

This paper introduces a compressed sensing framework combined with adaptive distance metric learning to improve the accuracy and efficiency of Magnetic Resonance Fingerprinting (MRF) in estimating tissue parameters from undersampled data.

Contribution

It proposes a novel CS-based approach for MRF that enhances robustness to low sampling ratios and employs learned distance metrics for better fingerprint matching.

Findings

Outperforms state-of-the-art methods in parameter estimation accuracy.

More robust to low sampling ratios in MR data.

Significantly improves fingerprint matching accuracy.

Abstract

Magnetic Resonance Fingerprinting (MRF) is a novel technique that simultaneously estimates multiple tissue-related parameters, such as the longitudinal relaxation time T1, the transverse relaxation time T2, off resonance frequency B0 and proton density, from a scanned object in just tens of seconds. However, the MRF method suffers from aliasing artifacts because it significantly undersamples the k-space data. In this work, we propose a compressed sensing (CS) framework for simultaneously estimating multiple tissue-related parameters based on the MRF method. It is more robust to low sampling ratio and is therefore more efficient in estimating MR parameters for all voxels of an object. Furthermore, the MRF method requires identifying the nearest atoms of the query fingerprints from the MR-signal-evolution dictionary with the L2 distance. However, we observed that the L2 distance is not always a proper metric to measure the similarities between MR Fingerprints. Adaptively learning a distance metric from the undersampled training data can significantly improve the matching accuracy of the query fingerprints. Numerical results on extensive simulated cases show that our method substantially outperforms stateof-the-art methods in terms of accuracy of parameter estimation.