Contrastive Learning for Accelerated MR Fingerprinting

Motivation: MR Fingerprinting (MRF) enables simultaneous multi-parametric mapping but suffers from computational challenges and noise/artifact sensitivity due to dictionary matching. Goal(s): This study aims to develop a novel network called CLIP-MRF to improve pattern matching in MRF. It incorporates contrastive learning to enhance quantification accuracy in accelerated MRF. Approach: We propose a dual-encoder contrastive training method to robustly map MRF signals to tissue parameters accurately. The model maximizes similarity between matching signal-parameter pairs and minimizes mismatched ones during training. Results: CLIP-MRF demonstrates superior performance over the state-of-the-art MRF methods in T1 and T2 quantification, reducing the reconstruction time and errors. Impact: The CLIP-MRF network enables accurate parameter mapping and improves computational efficiency for accelerated MRF. Trained on simulated data only, the network offers robust generalization across signals with different noise/artifacts, paving the way for fast and reliable tissue quantification.