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arXiv · 2201.09375

Deep Unrolling for Magnetic Resonance Fingerprinting

Dongdong Chen·Mike E. Davies·Mohammad Golbabaee
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Abstract

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.

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Dongdong Chen, Mike E. Davies, Mohammad Golbabaee. 2022-01-23. Deep Unrolling for Magnetic Resonance Fingerprinting. https://arxiv.org/abs/2201.09375

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