Faculty, Staff and Student Publications
Publication Date
1-1-2021
Journal
AMIA Annual Symposium Proceedings
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. 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.
Keywords
Humans, Image Processing, Computer-Assisted, Phantoms, Imaging, Tomography, X-Ray Computed
PMID
35308983
PMCID
PMC8861670
PubMedCentral® Posted Date
2-21-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
