# Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval

arXiv: https://arxiv.org/abs/1908.11506 · HTML: https://mlsanity.com/paper/1908.11506/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 11 of 11)

- [A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis](https://mlsanity.com/paper/2201.07646/) (2022)
- [SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks](https://mlsanity.com/paper/2106.02599/) (2021)
- [Super-resolution of biomedical volumes with 2D supervision](https://mlsanity.com/paper/2404.09425/) (2024)
- [Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks](https://mlsanity.com/paper/2102.09386/) (2021)
- [Concept Activation Vectors for Generating User-Defined 3D Shapes](https://mlsanity.com/paper/2205.02102/) (2022)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [Is Autoencoder Truly Applicable for 3D CT Super-Resolution?](https://mlsanity.com/paper/2302.10272/) (2023)
- [Causal Image Synthesis of Brain MR in 3D](https://mlsanity.com/paper/2303.14349/) (2023)
- [Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation](https://mlsanity.com/paper/2307.10182/) (2023)
- [Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest](https://mlsanity.com/paper/2501.15572/) (2025)
- [Memory-Efficient 3D High-Resolution Medical Image Synthesis Using CRF-Guided GANs](https://mlsanity.com/paper/2503.10899/) (2025)

## Abstract (arXiv)

Many CT slice images are stored with large slice intervals to reduce storage size in clinical practice. This leads to low resolution perpendicular to the slice images (i.e., z-axis), which is insufficient for 3D visualization or image analysis. In this paper, we present a novel architecture based on conditional Generative Adversarial Networks (cGANs) with the goal of generating high resolution images of main body parts including head, chest, abdomen and legs. However, GANs are known to have a difficulty with generating a diversity of patterns due to a phenomena known as mode collapse. To overcome the lack of generated pattern variety, we propose to condition the discriminator on the different body parts. Furthermore, our generator networks are extended to be three dimensional fully convolutional neural networks, allowing for the generation of high resolution images from arbitrary fields of view. In our verification tests, we show that the proposed method obtains the best scores by PSNR/SSIM metrics and Visual Turing Test, allowing for accurate reproduction of the principle anatomy in high resolution. We expect that the proposed method contribute to effective utilization of the existing vast amounts of thick CT images stored in hospitals.