A temporal multiscale approach for MR Fingerprinting

TL;DR

This paper introduces a temporal multiscale method for quantitative MRI that reduces computational costs by approximating gradients based on data smoothness and employing a coarse-to-fine strategy, improving efficiency and accuracy.

Contribution

It presents a novel multiscale approach that approximates likelihood gradients using temporal smoothness, enhancing qMRI reconstruction efficiency and accuracy.

Findings

Reduces computation time in qMRI reconstruction.

Improves accuracy over single-scale methods.

Effective coarse-to-fine optimization strategy.

Abstract

Quantitative MRI (qMRI) is becoming increasingly important for research and clinical applications, however, state-of-the-art reconstruction methods for qMRI are computationally prohibitive. We propose a temporal multiscale approach to reduce computation times in qMRI. Instead of computing exact gradients of the qMRI likelihood, we propose a novel approximation relying on the temporal smoothness of the data. These gradients are then used in a coarse-to-fine (C2F) approach, for example using coordinate descent. The C2F approach was also found to improve the accuracy of solutions, compared to similar methods where no multiscaling was used.