MRI Super-Resolution with Deep Learning: A Comprehensive Survey
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908686169931776 |
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| author | Khateri, Mohammad Vasylechko, Serge Ghahremani, Morteza Timms, Liam Kocanaogullari, Deniz Warfield, Simon K. Jaimes, Camilo Karimi, Davood Sierra, Alejandra Tohka, Jussi Kurugol, Sila Afacan, Onur |
| author_facet | Khateri, Mohammad Vasylechko, Serge Ghahremani, Morteza Timms, Liam Kocanaogullari, Deniz Warfield, Simon K. Jaimes, Camilo Karimi, Davood Sierra, Alejandra Tohka, Jussi Kurugol, Sila Afacan, Onur |
| contents | High-resolution (HR) magnetic resonance imaging (MRI) is crucial for many clinical and research applications. However, achieving it remains costly and constrained by technical trade-offs and experimental limitations. Super-resolution (SR) presents a promising computational approach to overcome these challenges by generating HR images from more affordable low-resolution (LR) scans, potentially improving diagnostic accuracy and efficiency without requiring additional hardware. This survey reviews recent advances in MRI SR techniques, with a focus on deep learning (DL) approaches. It examines DL-based MRI SR methods from the perspectives of computer vision, computational imaging, inverse problems, and MR physics, covering theoretical foundations, architectural designs, learning strategies, benchmark datasets, and performance metrics. We propose a systematic taxonomy to categorize these methods and present an in-depth study of both established and emerging SR techniques applicable to MRI, considering unique challenges in clinical and research contexts. We also highlight open challenges and directions that the community needs to address. Additionally, we provide a collection of essential open-access resources, tools, and tutorials, available on our GitHub: https://github.com/mkhateri/Awesome-MRI-Super-Resolution.
IEEE keywords: MRI, Super-Resolution, Deep Learning, Computational Imaging, Inverse Problem, Survey. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MRI Super-Resolution with Deep Learning: A Comprehensive Survey Khateri, Mohammad Vasylechko, Serge Ghahremani, Morteza Timms, Liam Kocanaogullari, Deniz Warfield, Simon K. Jaimes, Camilo Karimi, Davood Sierra, Alejandra Tohka, Jussi Kurugol, Sila Afacan, Onur Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Signal Processing High-resolution (HR) magnetic resonance imaging (MRI) is crucial for many clinical and research applications. However, achieving it remains costly and constrained by technical trade-offs and experimental limitations. Super-resolution (SR) presents a promising computational approach to overcome these challenges by generating HR images from more affordable low-resolution (LR) scans, potentially improving diagnostic accuracy and efficiency without requiring additional hardware. This survey reviews recent advances in MRI SR techniques, with a focus on deep learning (DL) approaches. It examines DL-based MRI SR methods from the perspectives of computer vision, computational imaging, inverse problems, and MR physics, covering theoretical foundations, architectural designs, learning strategies, benchmark datasets, and performance metrics. We propose a systematic taxonomy to categorize these methods and present an in-depth study of both established and emerging SR techniques applicable to MRI, considering unique challenges in clinical and research contexts. We also highlight open challenges and directions that the community needs to address. Additionally, we provide a collection of essential open-access resources, tools, and tutorials, available on our GitHub: https://github.com/mkhateri/Awesome-MRI-Super-Resolution. IEEE keywords: MRI, Super-Resolution, Deep Learning, Computational Imaging, Inverse Problem, Survey. |
| title | MRI Super-Resolution with Deep Learning: A Comprehensive Survey |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Signal Processing |
| url | https://arxiv.org/abs/2511.16854 |