  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.
