Deep Learning for Accelerated and Robust MRI Reconstruction: a Review

TL;DR

This review discusses recent deep learning methods for MRI reconstruction, highlighting advances in architectures that improve image quality, speed up scans, and address data challenges, with implications for clinical practice.

Contribution

It provides a comprehensive overview of recent DL approaches in MRI reconstruction, emphasizing their potential and current limitations.

Findings

Deep learning improves MRI image quality and reconstruction speed.

DL methods enhance robustness against data variability and biases.

Current challenges include model generalization and clinical translation.

Abstract

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction. It focuses on DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. These include end-to-end neural networks, pre-trained networks, generative models, and self-supervised methods. The paper also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling subtle bias. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.