Deep Learning for Accelerated and Robust MRI Reconstruction: a Review
It addresses the need for faster and more robust MRI in clinical settings, but as a review, it is incremental in synthesizing existing research rather than presenting new findings.
This review paper tackles the problem of improving MRI reconstruction for diagnostic radiology by summarizing recent deep learning advances, highlighting methods that enhance image quality and accelerate scans, though it does not report specific numerical results.
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.