Keywords: MR Fingerprinting, MR Fingerprinting, deep learning
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.
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