AI-Powered MRI: Deep Learning Super-Resolution Revolutionizes Medical Imaging
By Mohammad Khateri, Serge Vasylechko, Morteza Ghahremani, Liam Timms, Deniz Kocanaogullari, Simon K. Warfield, Camilo Jaimes, Davood Karimi, Alejandra Sierra, Jussi Tohka, Sila Kurugol, Onur Afacan
Published on November 24, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CV updates on arXiv.org.
Summary
This comprehensive survey delves into the latest advancements in applying deep learning (DL) to Magnetic Resonance Imaging (MRI) super-resolution (SR). It highlights SR as a computational solution to overcome the high costs and technical limitations of acquiring high-resolution MRI scans, enabling the generation of detailed images from more affordable low-resolution data. The paper systematically categorizes DL-based MRI SR methods, exploring them through the multifaceted lenses of computer vision, computational imaging, inverse problems, and MR physics. It covers theoretical underpinnings, architectural designs, learning strategies, benchmark datasets, and performance metrics, while also addressing unique challenges in clinical contexts, identifying open research directions, and providing a curated collection of open-access resources.
Why It Matters
This research holds profound significance for AI professionals, signaling a critical leap towards democratizing and enhancing medical diagnostics worldwide. By enabling the creation of high-resolution MRI scans from more accessible low-resolution inputs via deep learning, it promises to substantially reduce healthcare costs and broaden access to advanced imaging, particularly in underserved global communities. For AI engineers, this goes beyond mere image enhancement; it represents a major push for AI into safety-critical applications where model robustness, interpretability, and rigorous clinical validation are paramount. It reinforces a growing industry trend where AI augments the capabilities of existing hardware, making expensive medical equipment more versatile and efficient without necessitating costly physical upgrades. This intricate fusion of computer vision, computational imaging, and medical physics also creates fertile ground for novel AI architectural innovations and advanced research into inverse problems. Professionals in the AI space should recognize the immense potential for improving patient outcomes, thoughtfully engage with the ethical considerations surrounding AI-generated diagnostic data, and seize the burgeoning career opportunities in developing and deploying these transformative AI solutions within healthcare. The overarching trend is clear: AI is increasingly becoming an indispensable tool for maximizing the value and utility of existing technological infrastructure, particularly in resource-constrained yet high-impact sectors like medicine.