Summary of Enhance the Image: Super Resolution Using Artificial Intelligence in Mri, by Ziyu Li et al.
Enhance the Image: Super Resolution using Artificial Intelligence in MRI
by Ziyu Li, Zihan Li, Haoxiang Li, Qiuyun Fan, Karla L. Miller, Wenchuan Wu, Akshay S. Chaudhari, Qiyuan Tian
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Medical Physics (physics.med-ph)
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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 techniques are explored to improve spatial resolution in magnetic resonance imaging (MRI), using convolutional neural networks, generative adversarial networks, transformers, diffusion models, and implicit neural representations. The chapter also delves into the impact of super-resolved images on clinical and neuroscientific assessments, as well as practical topics such as network architectures, image evaluation metrics, loss functions, and training data specifics. Challenges and potential future directions are discussed to facilitate the wider adoption of deep learning-based MRI super-resolution for various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MRI can be improved by using special computer models that make the pictures clearer. These models are called deep learning techniques. The chapter talks about how these models work and what they do. It also looks at how making the pictures clearer affects how doctors and scientists look at the images. Some technical details are discussed, like how to train the models and what kind of data is needed. |
Keywords
» Artificial intelligence » Deep learning » Super resolution
