Cross-Vendor CT Image Data Harmonization Using CVH-CT
This addresses a critical bottleneck for cross-center radiomics research by enabling more consistent data analysis, though it is an incremental improvement over existing methods.
The paper tackles the problem of harmonizing CT images from different scanner vendors to reduce variability in radiomics studies, proposing CVH-CT, a deep learning approach that uses self-attention and a VGG feature-based domain loss, and shows it effectively reduces scanner-related variability in radiomic features.
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.