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Abstract #2742

Contrastive Learning for Accelerated MR Fingerprinting

Peizhou Huang1, Brendan L. Eck2, Mingrui Yang2, Ruiying Liu3, Xiaoliang Zhang1, Xiaojuan Li2, and Leslie Ying1,3
1Biomedical Engineering, University at Buffalo, Buffalo, NY, United States, 2Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, United States, 3Electrical Engineering, University at Buffalo, Buffalo, NY, United States

Synopsis

Keywords: MR Fingerprinting, AI/ML Image Reconstruction, Contrastive Learning

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.

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Keywords