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.
@article{arxiv.2511.16854,
title = {MRI Super-Resolution with Deep Learning: A Comprehensive Survey},
author = {Mohammad Khateri and Serge Vasylechko and Morteza Ghahremani and Liam Timms and Deniz Kocanaogullari and Simon K. Warfield and Camilo Jaimes and Davood Karimi and Alejandra Sierra and Jussi Tohka and Sila Kurugol and Onur Afacan},
journal= {arXiv preprint arXiv:2511.16854},
year = {2025}
}