Summary of Deep Learning For Accelerated and Robust Mri Reconstruction: a Review, by Reinhard Heckel et al.
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review
by Reinhard Heckel, Mathews Jacob, Akshay Chaudhari, Or Perlman, Efrat Shimron
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep learning (DL) has revolutionized magnetic resonance imaging (MRI), a vital tool in diagnostic radiology. This review paper provides an exhaustive overview of recent breakthroughs in DL for MRI reconstruction, highlighting approaches and architectures designed to enhance image quality, accelerate scans, and tackle data-related challenges. The paper discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling subtle bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning is helping make magnetic resonance imaging (MRI) better. This review looks at how deep learning can improve MRI images, make scans faster, and solve problems with data. It talks about different types of neural networks that can help achieve these goals. The paper also discusses the potential benefits of using deep learning in MRI, including making it more accurate and reliable. |
Keywords
» Artificial intelligence » Deep learning
