# Self Super-Resolution for Magnetic Resonance Images using Deep Networks

arXiv: https://arxiv.org/abs/1802.09431 · HTML: https://mlsanity.com/paper/1802.09431/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 7 of 7)

- [SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks](https://mlsanity.com/paper/2106.02599/) (2021)
- [Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images](https://mlsanity.com/paper/2106.15395/) (2021)
- [Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI](https://mlsanity.com/paper/2202.09258/) (2022)
- [Self-supervised arbitrary scale super-resolution framework for anisotropic MRI](https://mlsanity.com/paper/2305.01360/) (2023)
- [Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation](https://mlsanity.com/paper/2203.10773/) (2022)
- [TV-based Deep 3D Self Super-Resolution for fMRI](https://mlsanity.com/paper/2410.04097/) (2024)
- [CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images](https://mlsanity.com/paper/2303.11831/) (2023)

## Abstract (arXiv)

High resolution magnetic resonance~(MR) imaging~(MRI) is desirable in many clinical applications, however, there is a trade-off between resolution, speed of acquisition, and noise. It is common for MR images to have worse through-plane resolution~(slice thickness) than in-plane resolution. In these MRI images, high frequency information in the through-plane direction is not acquired, and cannot be resolved through interpolation. To address this issue, super-resolution methods have been developed to enhance spatial resolution. As an ill-posed problem, state-of-the-art super-resolution methods rely on the presence of external/training atlases to learn the transform from low resolution~(LR) images to high resolution~(HR) images. For several reasons, such HR atlas images are often not available for MRI sequences. This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image. We use a blurred version of the input image to create training data for a state-of-the-art super-resolution deep network. The trained network is applied to the original input image to estimate the HR image. Our SSR result shows a significant improvement on through-plane resolution compared to competing SSR methods.