  Magnetic resonance Fingerprinting (MRF) is a relatively new multi-parametric
quantitative imaging method that involves a two-step process: (i)
reconstructing a series of time frames from highly-undersampled non-Cartesian
spiral k-space data and (ii) pattern matching using the time frames to infer
tissue properties (e.g., T1 and T2 relaxation times). In this paper, we
introduce a novel end-to-end deep learning framework to seamlessly map the
tissue properties directly from spiral k-space MRF data, thereby avoiding
time-consuming processing such as the nonuniform fast Fourier transform (NUFFT)
and the dictionary-based Fingerprint matching. Our method directly consumes the
non-Cartesian k- space data, performs adaptive density compensation, and
predicts multiple tissue property maps in one forward pass. Experiments on both
2D and 3D MRF data demonstrate that quantification accuracy comparable to
state-of-the-art methods can be accomplished within 0.5 second, which is 1100
to 7700 times faster than the original MRF framework. The proposed method is
thus promising for facilitating the adoption of MRF in clinical settings.
