# Cross-Vendor CT Image Data Harmonization Using CVH-CT

arXiv: https://arxiv.org/abs/2110.09693 · HTML: https://mlsanity.com/paper/2110.09693/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)](https://mlsanity.com/paper/1808.04256/) (2018)
- [An Introduction to Image Synthesis with Generative Adversarial Nets](https://mlsanity.com/paper/1803.04469/) (2018)
- [STAN-CT: Standardizing CT Image using Generative Adversarial Network](https://mlsanity.com/paper/2004.01307/) (2020)
- [CT Image Harmonization for Enhancing Radiomics Studies](https://mlsanity.com/paper/2107.01337/) (2021)

## Built on by (most cited first, 8 of 8)

- [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)
- [Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization](https://mlsanity.com/paper/2507.19858/) (2025)
- [Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images](https://mlsanity.com/paper/2409.02770/) (2024)
- [Texture-Aware StarGAN for CT data harmonisation](https://mlsanity.com/paper/2503.15058/) (2025)
- [Inter-vendor harmonization of Computed Tomography (CT) reconstruction kernels using unpaired image translation](https://mlsanity.com/paper/2309.12953/) (2023)
- [Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction](https://mlsanity.com/paper/2504.10080/) (2025)
- [Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels](https://mlsanity.com/paper/2505.22568/) (2025)

## Abstract (arXiv)

While remarkable advances have been made in Computed Tomography (CT), most of the existing efforts focus on imaging enhancement while reducing radiation dose. How to harmonize CT image data captured using different scanners is vital in cross-center large-scale radiomics studies but remains the boundary to explore. Furthermore, the lack of paired training image problem makes it computationally challenging to adopt existing deep learning models. %developed for CT image standardization. %this problem more challenging. We propose a novel deep learning approach called CVH-CT for harmonizing CT images captured using scanners from different vendors. The generator of CVH-CT uses a self-attention mechanism to learn the scanner-related information. We also propose a VGG feature-based domain loss to effectively extract texture properties from unpaired image data to learn the scanner-based texture distributions. The experimental results show that CVH-CT is clearly better than the baselines because of the use of the proposed domain loss, and CVH-CT can effectively reduce the scanner-related variability in terms of radiomic features.