A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning

TL;DR

This paper reviews recent optimization-based deep learning algorithms for MRI reconstruction, highlighting their potential to improve image quality and reconstruction speed, and aims to guide future research in this field.

Contribution

It provides the first comprehensive survey of optimization-based deep learning models specifically designed for MRI reconstruction.

Findings

Identifies key optimization algorithms used in MRI deep learning

Highlights advancements in reconstruction accuracy and speed

Provides a roadmap for future research directions

Abstract

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.