Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting

TL;DR

This paper introduces a convolutional neural network approach for rapid, accurate, and spatially regularized reconstruction of parametric maps in magnetic resonance fingerprinting, improving clinical feasibility.

Contribution

It presents a novel deep learning-based reconstruction method that outperforms existing techniques and incorporates spatial regularization for MRF parametric map estimation.

Findings

Achieved low normalized root mean squared errors for T1 and fat fraction maps.

Demonstrated robustness to heterogeneous datasets and unseen anatomical regions.

Outperformed state-of-the-art deep learning methods in MRF reconstruction.

Abstract

Magnetic resonance fingerprinting (MRF) provides a unique concept for simultaneous and fast acquisition of multiple quantitative MR parameters. Despite acquisition efficiency, adoption of MRF into the clinics is hindered by its dictionary matching-based reconstruction, which is computationally demanding and lacks scalability. Here, we propose a convolutional neural network-based reconstruction, which enables both accurate and fast reconstruction of parametric maps, and is adaptable based on the needs of spatial regularization and the capacity for the reconstruction. We evaluated the method using MRF T1-FF, an MRF sequence for T1 relaxation time of water (T1H2O) and fat fraction (FF) mapping. We demonstrate the method's performance on a highly heterogeneous dataset consisting of 164 patients with various neuromuscular diseases imaged at thighs and legs. We empirically show the benefit of incorporating spatial regularization during the reconstruction and demonstrate that the method learns meaningful features from MR physics perspective. Further, we investigate the ability of the method to handle highly heterogeneous morphometric variations and its generalization to anatomical regions unseen during training. The obtained results outperform the state-of-the-art in deep learning-based MRF reconstruction. The method achieved normalized root mean squared errors of 0.048 $\pm$ 0.011 for T1H2O maps and 0.027 $\pm$ 0.004 for FF maps when compared to the dictionary matching in a test set of 50 patients. Coupled with fast MRF sequences, the proposed method has the potential of enabling multiparametric MR imaging in clinically feasible time.