Improving quantitative MRI using self‐supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping

• Published in: Magnetic resonance in medicine Vol. 92; no. 1; pp. 98 - 111
• Main Authors: Bian, Wanyu, Jang, Albert, Liu, Fang
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.07.2024
• Subjects: Algorithms; Brain - diagnostic imaging; Deep Learning; Humans; Image Processing, Computer-Assisted - methods; Image reconstruction; Knee - diagnostic imaging; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mapping; Mathematical models; Medical imaging; Parameters; Phantoms, Imaging; Reinforcement; Supervised learning; Supervised Machine Learning; self‐supervised learning; quantitative MRI; model reinforcement; optimization
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.30045

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. Unlike conventional deep learning methods which require large amounts of training data, RELAX-MORE is a subject-specific method that can be trained on single-subject data through self-supervised learning, making it accessible and practically applicable to many qMRI studies. Using quantitative mapping as an example, the proposed method was applied to the brain, knee and phantom data. 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. Compared with other state-of-the-art conventional and deep learning methods, RELAX-MORE significantly improves efficiency, accuracy, robustness, and generalizability for rapid MR parameter mapping. 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.