Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative MR imaging approach. Deep learning methods have been proposed for MRF and demonstrated improved performance over classical compressed sensing algorithms. However many of these end-to-end models are physics-free, while consistency of the predictions with respect to the physical forward model is crucial for reliably solving inverse problems. To address this, recently [1] proposed a proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within an unrolled learning mechanism. However, [1] only evaluated the unrolled model on synthetic data using Cartesian sampling trajectories. In this paper, as a complementary to [1], we investigate other choices of encoders to build the proximal neural network, and evaluate the deep unrolling algorithm on real accelerated MRF scans with non-Cartesian k-space sampling trajectories.
@article{arxiv.2201.09375,
title = {Deep Unrolling for Magnetic Resonance Fingerprinting},
author = {Dongdong Chen and Mike E. Davies and Mohammad Golbabaee},
journal= {arXiv preprint arXiv:2201.09375},
year = {2022}
}
Comments
Tech report. arXiv admin note: substantial text overlap with arXiv:2006.15271