Cross-Vendor CT Image Data Harmonization Using CVH-CT

TL;DR

This paper introduces CVH-CT, a deep learning method that harmonizes CT images from different scanners without paired data, improving radiomic consistency across vendors.

Contribution

The paper presents a novel CVH-CT model with self-attention and VGG-based domain loss for unpaired CT harmonization across vendors.

Findings

CVH-CT outperforms baseline methods in reducing scanner variability.

The model effectively preserves radiomic features across different scanners.

Experimental results demonstrate improved image harmonization quality.

Abstract

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.