Deep Unrolling for Magnetic Resonance Fingerprinting

Dongdong Chen, Mike E. Davies, Mohammad Golbabaee

Research output: Contribution to conferencePaperpeer-review


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.
Original languageEnglish
Publication statusPublished - 17 Apr 2022
EventIEEE International Symposium on Biomedical Imaging - Kolkata, India
Duration: 17 Apr 2022 → …


ConferenceIEEE International Symposium on Biomedical Imaging
Abbreviated titleISBI
Period17/04/22 → …


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