Scalable Zero-Shot Self-Supervised Learning for Accelerated MR Fingerprinting

Motivation: Collecting ground truth tissue property maps for training deep learning networks for MR Fingerprinting reconstruction can be challenging. Goal(s): We aim to reduce reliance on training datasets for deep learning based MR Fingerprinting. Approach: We propose a novel zero-shot self-supervised MRF framework that requires only the undersampled k-space measurements. We develop strategies to rapidly retrieve fingerprints from the dictionary for efficient network training. Results: Our method demonstrates promising results on 2x and 4x accelerated MRF without requiring supervised learning based on ground truth tissue property maps, laborious reconstruction, explicit dictionary matching, and network pre-training. Impact: Our preliminary results validated the feasibility of zero-shot self-supervised MRF reconstruction from undersampled MRF data with the help of physics-based constraints.