Traditional methods of quantitative analysis of CT images typically involve working with patient data, which is often expensive and limited in terms of ground truth. To counter these restrictions, quantitative assessments can instead be made through Virtual Imaging Trials (VITs) which simulate the CT imaging process. This study sought to validate DukeSim (a scanner-specific CT simulator) utilizing clinically relevant biomarkers for a customized anthropomorphic chest phantom. The physical phantom was imaged utilizing two commercial CT scanners (Siemens Somatom Force and Definition Flash) with varying imaging parameters. A computational version of the phantom was simulated utilizing DukeSim for each corresponding real acquisition. Biomarkers were computed and compared between the real and virtually acquired CT images to assess the validity of DukeSim. The simulated images closely matched the real images both qualitatively and quantitatively, with the average biomarker percent difference of 3.84% (range 0.19% to 18.27%). Results showed that DukeSim is reasonably well validated across various patient imaging conditions and scanners, which indicates the utility of DukeSim for further VIT studies where real patient data may not be feasible.
CT imaging provides physicians valuable insights when diagnosing disease in a clinical setting. In order to provide an accurate diagnosis, is it important to have a high accuracy with controlled variability across CT scans from different scanners and imaging parameters. The purpose of this study was to analyze variability of lung imaging biomarkers across various scanners and parameters using a customized version of a commercially available anthropomorphic chest Phantom (Kyoto Kagaku) with several experimental sample inserts. The phantom was across 10 different CT scanners with a total of 209 imaging conditions. An algorithm was developed to compute different imaging biomarkers. Variability across images from the same scanner and from different scanners was analyzed by computing coefficients of variation (CV) and standard deviations of HU values. LAA -950 and LAA -856 biomarkers had the highest levels of variability, while the majority of other biomarkers had variability less than 10 HU or 10% CV in both inter and intrascan measurements. There was no clear trend present between the biomarker measurements and CTDIvol. The results of this study demonstrates the existing variability in CT quantifications for lung imaging, which prompt further studies on how to reduce such variation.
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