Physics-Based Decoding Improves Magnetic Resonance Fingerprinting

Juyeon Heo, Pingfan Song, Weiyang Liu, Adrian Weller

Abstract: Magnetic Resonance Fingerprinting (MRF) is a promising approach for fast Quantitative Magnetic Resonance Imaging (QMRI). However, existing MRF methods suffer from slow imaging speeds and poor generalization performance on radio frequency pulse sequences generated in various scenarios. To address these issues, we propose a novel MRI physics-informed regularization for MRF. The proposed approach adopts a supervised encoder-decoder framework, where the encoder performs the main task, i.e. predicting the target tissue properties from input magnetic responses, and the decoder servers as a regularization via reconstructing the inputs from the estimated tissue properties using a Bloch-equation based MRF physics model. The physics-based decoder improves the generalization performance and uniform stability by a considerable margin in practical out-of-distribution settings. Extensive experiments verified the effectiveness of the proposed approach and achieved state-of-the-art performance on tissue property estimation.