Peer-Reviewed Article
Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging
Our Summary
This review paper explores existing literature on the diverse applications of deep learning reconstruction (DLR) techniques for fast MR neuroimaging. It provides a comprehensive overview of how DLR is being utilized to reduce scan times while maintaining or even enhancing image quality. The authors delve into various studies and technological advancements, presenting a detailed analysis of the progress and current state of DLR in the realm of neuroimaging.
Why this matters
Both vendor-specific and vendor-agnostic technologies are being widely used in a variety of clinical neuroimaging scenarios, demonstrating the broad applicability and impact of DLR. Publications report its successful implementation in the diagnosis and management of conditions such as Parkinson’s disease, pituitary adenoma, multiple sclerosis, and degenerative spine diseases, among others. The review also highlights SwiftMR as one of the available technologies in the field, emphasizing its role in advancing neuroimaging practices. This underscores the growing importance of DLR in enhancing diagnostic accuracy and efficiency across a range of neurological conditions.
Explore the full study to gain a deeper understanding of the diverse applications of deep learning reconstruction in fast MR neuroimaging and its significant impact on improving scan times and image quality.
Yoo, R.-E., & Choi, S. H. (2024). Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging. Magnetic Resonance in Medical Sciences. Article ID rev.2023-0153. Advance online publication April 27, 2024.
