# STAN-CT: Standardizing CT Image using Generative Adversarial Network

arXiv: https://arxiv.org/abs/2004.01307 · HTML: https://mlsanity.com/paper/2004.01307/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2020.

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

- [CT Image Harmonization for Enhancing Radiomics Studies](https://mlsanity.com/paper/2107.01337/) (2021)
- [Cross-Vendor CT Image Data Harmonization Using CVH-CT](https://mlsanity.com/paper/2110.09693/) (2021)
- [Latent Diffusion Model for Medical Image Standardization and Enhancement](https://mlsanity.com/paper/2310.05237/) (2023)
- [DiffusionCT: Latent Diffusion Model for CT Image Standardization](https://mlsanity.com/paper/2301.08815/) (2023)
- [A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images](https://mlsanity.com/paper/2403.04612/) (2024)
- [Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization](https://mlsanity.com/paper/2507.19858/) (2025)
- [Texture-Aware StarGAN for CT data harmonisation](https://mlsanity.com/paper/2503.15058/) (2025)
- [Multiscale Latent Diffusion Model for Enhanced Feature Extraction from Medical Images](https://mlsanity.com/paper/2410.04000/) (2024)
- [Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction](https://mlsanity.com/paper/2504.10080/) (2025)
- [Deep-Learning-Assisted Highly-Accurate COVID-19 Diagnosis on Lung Computed Tomography Images](https://mlsanity.com/paper/2507.04252/) (2025)
- [RA-CMF: Region-Adaptive Conditional MeanFlow for CT Image Reconstruction](https://mlsanity.com/paper/2605.00901/) (2026)

## Abstract (arXiv)

Computed tomography (CT) plays an important role in lung malignancy diagnostics and therapy assessment and facilitating precision medicine delivery. However, the use of personalized imaging protocols poses a challenge in large-scale cross-center CT image radiomic studies. We present an end-to-end solution called STAN-CT for CT image standardization and normalization, which effectively reduces discrepancies in image features caused by using different imaging protocols or using different CT scanners with the same imaging protocol. STAN-CT consists of two components: 1) a novel Generative Adversarial Networks (GAN) model that is capable of effectively learning the data distribution of a standard imaging protocol with only a few rounds of generator training, and 2) an automatic DICOM reconstruction pipeline with systematic image quality control that ensure the generation of high-quality standard DICOM images. Experimental results indicate that the training efficiency and model performance of STAN-CT have been significantly improved compared to the state-of-the-art CT image standardization and normalization algorithms.