Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting
This work addresses the time-consuming processing bottleneck in MRF for clinical applications, enabling faster adoption in medical imaging.
The paper tackles the slow processing of Magnetic Resonance Fingerprinting (MRF) by introducing an end-to-end deep learning framework that maps tissue properties directly from k-space data, achieving comparable accuracy to state-of-the-art methods in 0.5 seconds, which is 1100 to 7700 times faster than the original MRF framework.
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.