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Abstract #2608

Accelerating MR Fingerprinting with Self-Supervised Learning Driven by Bloch Equations

Yilin Liu1, Yunkui Pang1, Yong Chen2, and Pew-Thian Yap3,4
1Department of Computer Science, University of North Carolina at Chapel hill, Chapel Hill, NC, United States, 2Radiology, Case Western University, Cleveland, OH, United States, 3Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 4Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

Synopsis

Keywords: MR Fingerprinting, AI/ML Image Reconstruction, Bloch equations; Deep Image Prior

Motivation: Collecting in vivo training datasets for deep-learning-based MR Fingerprinting (MRF) reconstruction and tissue mapping methods can be challenging.

Goal(s): We aim to shorten the MRF acquisition using deep learning methods without relying on training datasets.

Approach: We propose a new self-supervised MRF deep learning framework that requires only undersampled k-space measurements. MRF dictionary is incorporated as a physics constraint to regularize the reconstruction in an efficient manner.

Results: Our method shows promising results on 4x accelerated in-vivo MRF scans and phantom data, achieving ~6 mins post-processing time per scan without requiring ground truth tissue property maps or network pre-training.

Impact: Our method achieves better results than conventional dictionary matching and is faster than current self-supervised MRF methods.

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Keywords