Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement

TL;DR

RELAX-MORE is a self-supervised deep learning method that unrolls model-based MRI reconstruction into a framework capable of producing accurate, robust, and rapid quantitative MR parameter maps from single-subject data, enhancing clinical applicability.

Contribution

The paper introduces RELAX-MORE, a novel self-supervised learning approach that enables subject-specific MRI parameter mapping without large training datasets, improving efficiency and robustness.

Findings

Achieves high accuracy in MR parameter maps

Corrects imaging artifacts and removes noise

Outperforms existing methods in speed and robustness

Abstract

This paper proposes a novel self-supervised learning method, RELAX-MORE, for quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll a model-based qMRI reconstruction into a deep learning framework, enabling the generation of highly accurate and robust MR parameter maps at imaging acceleration. Unlike conventional deep learning methods requiring a large amount 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 the quantitative $T_1$ mapping as an example at different brain, knee and phantom experiments, the proposed method demonstrates excellent performance in reconstructing MR parameters, correcting imaging artifacts, removing noises, and recovering image features at imperfect imaging 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, with great potential to enhance the clinical translation of qMRI.