TransUnet Based Deep Learning with Tissue-weighted Loss Function for Accelerated Magnetic Resonance Fingerprint Reconstruction

Motivation: MRF data with short time frames make dictionary matching challenging with large quantitative errors. Goal(s): We proposed a deep learning method by taking account in global Information and tissue-specific relaxometry, for better quantitative results using shorter frames than dictionary matching with good reproducibility. Approach: We proposed to combine Unet with an embedded transformer and a tissue-weighted loss function. We compared our approach to dictionary matching and examined various levels of reduced scan time. Results: The proposed method has less error with shorter frame data comparing dictionary matching. This is more effective for the gray matter and for scan times shorter than 300 frames. Impact: The proposed method improves the problem of poor quality of reconstructed images for dictionary matching in shorter frames, in addition this method has good repeatability and provides a powerful reconstruction algorithm for MRF sequence acceleration.