Robust Highly-accelerated MR Fingerprinting Using Transformer-based Deep Learning

Motivation: MR fingerprinting (MRF) conventional reconstruction methods need a substantial reconstruction time and memory space. We aim to propose a novel deep-learning method for accelerated MRF reconstruction. Goal(s): To achieve more accurate quantification reconstruction for T1 and T2 from highly undersampled MRF data. Approach: A novel training process was also proposed to construct reliable training data with noise-like aliasing artifacts boosted by Transformer network without need to know the structure information. Results: Experimental results demonstrate that the proposed method achieves more accurate quantification for T1 and T2 than pattern matching and DRONE. Impact: The proposed method can generate more accurate quantitative maps for highly accelerated MRF data that enable clinical use in real application. In addition, the proposed training process is robust to different structures in the image to be reconstructed.