Cross-Vendor CT Image Data Harmonization Using CVH-CT

Md Selim, Jie Zhang, Baowei Fei, Guo Qiang Zhang, Gary Yeeming Ge, Jin Chen

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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. 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.

Original languageEnglish
Pages (from-to)1099-1108
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
StatePublished - 2021

Bibliographical note

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ASJC Scopus subject areas

  • General Medicine


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