Automated Design of Pulse Sequences for Magnetic Resonance Fingerprinting using Physics-Inspired Optimization

TL;DR

This paper introduces a physics-inspired optimization method for designing magnetic resonance fingerprinting pulse sequences, resulting in faster scans and sequences robust against artifacts, surpassing prior manually designed sequences.

Contribution

It presents a novel automated design approach for MRF pulse sequences using physics-based optimization heuristics, improving speed and robustness over traditional methods.

Findings

Achieved fourfold reduction in scan time compared to prior sequences.

Discovered MRF sequences with intrinsic robustness to shading artifacts.

Sequences exhibit qualitatively different features from previous designs.

Abstract

Magnetic Resonance Fingerprinting (MRF) is a method to extract quantitative tissue properties such as T1 and T2 relaxation rates from arbitrary pulse sequences using conventional magnetic resonance imaging hardware. MRF pulse sequences have thousands of tunable parameters which can be chosen to maximize precision and minimize scan time. Here we perform de novo automated design of MRF pulse sequences by applying physics-inspired optimization heuristics. Our experimental data suggests systematic errors dominate over random errors in MRF scans under clinically-relevant conditions of high undersampling. Thus, in contrast to prior optimization efforts, which focused on statistical error models, we use a cost function based on explicit first-principles simulation of systematic errors arising from Fourier undersampling and phase variation. The resulting pulse sequences display features qualitatively different from previously used MRF pulse sequences and achieve fourfold shorter scan time than prior human-designed sequences of equivalent precision in T1 and T2. Furthermore, the optimization algorithm has discovered the existence of MRF pulse sequences with intrinsic robustness against shading artifacts due to phase variation.