Deep learning for accelerated and robust MRI reconstruction
importance 6/10
ai assisted
Advanced MRI Techniques and Applications medical imaging stage: research
Problem
MRI scans are slow and traditional reconstruction faces image quality, acceleration and data-related challenges.
Why it matters
MRI is a critical diagnostic tool, so faster and more robust scans affect patient care.
What the AI did
Review of deep learning approaches (end-to-end networks, pre-trained and generative models, self-supervised methods) for improving MRI image quality and accelerating scans.
Results
not stated in source
Impact
Review concludes DL has potential to significantly impact clinical MRI practice, with stated limitations.
Record
| Venue | Magnetic Resonance Materials in Physics Biology and Medicine |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Reinhard Heckel, Mathews Jacob, Akshay Sanjay Chaudhari, Or Perlman, Efrat Shimron |
| Citations | 119 (OpenAlex, at import time) |
| References | 324 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1007/s10334-024-01173-8 |
| Open access | hybrid |
| Keywords | deep learning, MRI reconstruction, medical imaging, generative models |
Importance rating
6
significant — thousands to millions affected
Abstract calls MRI a critical tool in diagnostic radiology; faster, higher-quality scans broadly affect patient care, but no scale or numbers are given and this is a review.