Improving quantitative MRI using self-supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping.
Journal:
Magnetic resonance in medicine
Published Date:
Feb 11, 2024
Abstract
PURPOSE: This paper proposes a novel self-supervised learning framework that uses model reinforcement, REference-free LAtent map eXtraction with MOdel REinforcement (RELAX-MORE), for accelerated quantitative MRI (qMRI) reconstruction. 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.