mlsanityData to 14 Oct 2026

MRI Super-Resolution with Deep Learning: A Comprehensive Survey

arXiv 2511.16854 ↗First posted 20 Nov 2025eess.IV cs.AI cs.CVcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 194 of its 470 references that are in the map. First posted on arXiv 20 Nov 2025.

Builds on

194 of 470 references.

References · 194

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38 more
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50 most cited of 194 All 194 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 6.

7 papers.

  1. 2025Efficient Vision Mamba for MRI Super-Resolution via Hybrid Selective Scanningcited by 1
  2. 2026USFetal: Tools for Fetal Brain Ultrasound Compoundingcited by 1
  3. 2026CRIS: Cross-Plane Self-Supervised Isotropic Restoration for Anisotropic Volumetric Imaging Across Modalitiescited by 0
  4. 2026PhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolutioncited by 0
  5. 2026Cardiac MRI Through-Plane Super-Resolution Guided by Reference and Memorycited by 0
  6. 2026MRI super-resolution in ten sampling steps using a diffusion bridge modelcited by 0
  7. 2026AnaDiffusion: Anatomically CompositionalLatent Diffusion for Controllable 3D Brain MRI Generationcited by 0