  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.
