Keywords: Quantitative Imaging, Quantitative Imaging, Model-based Reconstruction, Relaxometry, Brain, Self-supervised Learning
Motivation: Quantitative MRI (qMRI) is time-consuming and requires substantial efforts for acceleration to cut down the acquisition time.
Goal(s): This paper proposes a novel self-supervised learning framework that uses model reinforcement, RELAX-MORE, for accelerated qMRI reconstruction.
Approach: The proposed method uses an optimization algorithm to unroll an iterative model-based qMRI reconstruction into a deep learning framework, enabling accelerated MR parameter maps that are highly accurate and robust.
Results: The proposed method generates high quality MR parameter maps that correct for image artifacts, removes noise, and recovers image features in regions of imperfect image conditions.
Impact: This work demonstrates the feasibility of a new self-supervised learning method for rapid MR parameter mapping, that is readily adaptable to the clinical translation of qMRI.
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