mlsanityData to 7 Oct 2026

Enhance the Image: Super Resolution using Artificial Intelligence in MRI

arXiv 2406.13625 ↗First posted 19 Jun 2024cs.CV cs.AI physics.med-phcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI, ranging from convolutional neural networks, generative adversarial networks, to more advanced models including transformers, diffusion models, and implicit neural representations. Our exploration extends beyond the methodologies to scrutinize the impact of super-resolved images on clinical and neuroscientific assessments. We also cover various practical topics such as network architectures, image evaluation metrics, network loss functions, and training data specifics, including downsampling methods for simulating low-resolution images and dataset selection. Finally, we discuss existing challenges and potential future directions regarding the feasibility and reliability of deep learning-based MRI super-resolution, with the aim to facilitate its wider adoption to benefit various clinical and neuroscientific applications.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 50 of its 93 references that are in the map. First posted on arXiv 19 Jun 2024.

Builds on

50 of 93 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model23 Aug
  2. 2022Brain Imaging Generation with Latent Diffusion Models15 Sep

Other references · 48

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36 more
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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7

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  4. 2026Deep Learning for MRI Slice Interpolation: The Critical Role of Problem Formulation
  5. 2026Perceptually Regularized Diffusion Model for Image Super-Resolution
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  7. 2025Efficient Vision Mamba for MRI Super-Resolution via Hybrid Selective Scanning
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