Magnetic resonance imaging (MRI) is renowned for its exceptional soft tissue contrast and high spatial resolution, making it a pivotal tool in medical imaging. The integration of deep learning algorithms offers significant potential for optimizing MRI reconstruction processes. Despite the growing body of research in this area, a comprehensive survey of optimization-based deep learning models tailored for MRI reconstruction has yet to be conducted. This review addresses this gap by presenting a thorough examination of the latest optimization-based algorithms in deep learning specifically designed for MRI reconstruction. The goal of this paper is to provide researchers with a detailed understanding of these advancements, facilitating further innovation and application within the MRI community.
@article{arxiv.2406.02626,
title = {A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning},
author = {Wanyu Bian},
journal= {arXiv preprint arXiv:2406.02626},
year = {2024}
}