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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.13625 |
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| _version_ | 1866917811723436032 |
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| author | Li, Ziyu Li, Zihan Li, Haoxiang Fan, Qiuyun Miller, Karla L. Wu, Wenchuan Chaudhari, Akshay S. Tian, Qiyuan |
| author_facet | Li, Ziyu Li, Zihan Li, Haoxiang Fan, Qiuyun Miller, Karla L. Wu, Wenchuan Chaudhari, Akshay S. Tian, Qiyuan |
| contents | This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI, ranging from convolutional neural networks, generative adversarial networks, to more advanced models including transformers, diffusion models, and implicit neural representations. Our exploration extends beyond the methodologies to scrutinize the impact of super-resolved images on clinical and neuroscientific assessments. We also cover various practical topics such as network architectures, image evaluation metrics, network loss functions, and training data specifics, including downsampling methods for simulating low-resolution images and dataset selection. Finally, we discuss existing challenges and potential future directions regarding the feasibility and reliability of deep learning-based MRI super-resolution, with the aim to facilitate its wider adoption to benefit various clinical and neuroscientific applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13625 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhance the Image: Super Resolution using Artificial Intelligence in MRI Li, Ziyu Li, Zihan Li, Haoxiang Fan, Qiuyun Miller, Karla L. Wu, Wenchuan Chaudhari, Akshay S. Tian, Qiyuan Computer Vision and Pattern Recognition Artificial Intelligence Medical Physics This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI, ranging from convolutional neural networks, generative adversarial networks, to more advanced models including transformers, diffusion models, and implicit neural representations. Our exploration extends beyond the methodologies to scrutinize the impact of super-resolved images on clinical and neuroscientific assessments. We also cover various practical topics such as network architectures, image evaluation metrics, network loss functions, and training data specifics, including downsampling methods for simulating low-resolution images and dataset selection. Finally, we discuss existing challenges and potential future directions regarding the feasibility and reliability of deep learning-based MRI super-resolution, with the aim to facilitate its wider adoption to benefit various clinical and neuroscientific applications. |
| title | Enhance the Image: Super Resolution using Artificial Intelligence in MRI |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Medical Physics |
| url | https://arxiv.org/abs/2406.13625 |