Validation and Application of a Virtual Imaging Trial Platform for Accurate and Precise CT Quantifications in Lung Imaging
Computed Tomography (CT) is a prevalent imaging technique in modern medicine that provides physicians a non-invasive method to evaluate and diagnose various clinical conditions. To aid in diagnosis, it is important to have a high accuracy and reliability in these images. In the first phase of this study, the variability of clinically-relevant imaging biomarkers was analyzed across different scanners and imaging parameters through usage of a customized anthropomorphic chest phantom with several experimental sample inserts. This phantom was scanned across 10 different scanners. Imaging biomarkers were computed for each scan. Intra and inter-scan variability was assessed by computing coefficients of variation and standard deviations of the measurements. It was found that LAA -950 and LAA -856 were the biomarkers with the highest levels of variability, while the majority of other biomarkers had variability less than 10 HU or 10% CV in both inter and intra-scan measurements. No clear trend was found between the variability of the biomarkers and radiation dose (i.e., CTDI).
Traditional assessments of CT technologies are limited in the sense that they work with real patient data and are not efficient. Alternatively, Virtual Imaging Trials (VITs), which use virtual scanners and patients, are more efficient and avoid unnecessary radiation exposure. DukeSim is a CT simulator that has been validated with simple cylindrical phantoms in the past, but not with more clinically-relevant phantoms and conditions. Biomarkers computed from real CT image data were compared to those from simulated CT scans of a computational version of an anthropomorphic chest phantom. Overall, relative percent errors ranged from 0.187% to 18.269%.
Having validated DukeSim in a clinically relevant context, the utility of DukeSim as a VIT tool was shown by investigating the effects of imaging and reconstruction parameters on the clinically relevant biomarkers. It was found that sharper reconstruction kernels and lower tube currents tended to reduce the accuracy of measured biomarkers. These findings will help to spark further studies in virtual imaging, which can help to yield further clinical insights to improve patient health outcomes.
Virtual Imaging Trial
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