GFB-MRF: A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting

TL;DR

This paper introduces a novel parallel regularization approach combining spatial, low-rank, and Bloch manifold constraints for MR Fingerprinting, significantly enhancing tissue map reconstruction quality especially in noisy, highly undersampled scenarios.

Contribution

It presents a new joint regularization framework for MRF reconstruction that effectively combines multiple priors in a single optimization process.

Findings

Improved tissue map accuracy on phantom and volunteer scans.

Enhanced robustness to noise in short sequence MRF reconstructions.

Significant quality gains over existing methods.

Abstract

Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction. The reason is that it is indeed challenging to incorporate effectively multiple regularizations in a single MRF optimization algorithm. As a result most of these methods are not robust to noise especially when the sequence length is short. In this paper, we propose a family of new methods where spatial and low-rank regularizations, in addition to the Bloch manifold regularization, are applied on the images. We show on digital phantom and NIST phantom scans, as well as volunteer scans that the proposed methods bring significant improvement in the quality of the estimated tissue maps.