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While remarkable advances have been made in Computed Tomography (CT), most ofthe existing efforts focus on imaging enhancement while reducing radiationdose. How to harmonize CT image data captured using different scanners is vitalin cross-center large-scale radiomics studies but remains the boundary toexplore. Furthermore, the lack of paired training image problem makes itcomputationally challenging to adopt existing deep learning models. %developedfor CT image standardization. %this problem more challenging. We propose anovel deep learning approach called CVH-CT for harmonizing CT images capturedusing scanners from different vendors. The generator of CVH-CT uses aself-attention mechanism to learn the scanner-related information. We alsopropose a VGG feature-based domain loss to effectively extract textureproperties from unpaired image data to learn the scanner-based texturedistributions. The experimental results show that CVH-CT is clearly better thanthe baselines because of the use of the proposed domain loss, and CVH-CT caneffectively reduce the scanner-related variability in terms of radiomicfeatures.

Cross-Vendor CT Image Data Harmonization Using CVH-CT