MRI Super-Resolution with Deep Learning: A Comprehensive Survey
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.
692d94148d366933b9cd1542
Mohammad Khateri
claimed_verified
2025-12-01T16:24:32.548Z
65eb6c814f8aa96f64e6d8af
/avatars/149621e3662b241a9c37612f51032875.svg
Mohammad Khateri
mkhateri
user
mkhateri
692d94148d366933b9cd1543
Serge Vasylechko
692d94148d366933b9cd1544
Morteza Ghahremani
692d94148d366933b9cd1545
Liam Timms
692d94148d366933b9cd1546
Deniz Kocanaogullari
692d94148d366933b9cd1547
Simon K. Warfield
692d94148d366933b9cd1548
Camilo Jaimes
692d94148d366933b9cd1549
Davood Karimi
692d94148d366933b9cd154a
Alejandra Sierra
692d94148d366933b9cd154b
Jussi Tohka
692d94148d366933b9cd154c
Sila Kurugol
692d94148d366933b9cd154d
Onur Afacan
MRI Super-Resolution with Deep Learning: A Comprehensive Survey
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.
A survey examines deep learning-based super-resolution techniques in MRI, addressing challenges and providing resources for generating high-resolution images from low-resolution scans.
deep learning MRI super-resolution computational imaging inverse problems benchmark datasets