Task‐based validation and application of a scanner‐specific CT simulator using an anthropomorphic phantom

• Published in: Medical physics (Lancaster) 2022-12, Vol.49 (12), p.7447-7457
• Main Authors: Shankar, Sachin S., Felice, Nicholas, Hoffman, Eric A., Atha, Jarron, Sieren, Jessica C., Samei, Ehsan, Abadi, Ehsan
• Format: Article
• Language: English
• Subjects: Computer Simulation; CT simulator; DukeSim; Humans; image quality validation; lung quantifications; Phantoms, Imaging; Radiation Dosage; Tomography Scanners, X-Ray Computed; Tomography, X-Ray Computed - methods; virtual imaging trial
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.15967

Background Quantitative analysis of computed tomography (CT) images traditionally utilizes real patient data that can pose challenges with replicability, efficiency, and radiation exposure. Instead, virtual imaging trials (VITs) can overcome these hurdles through computer simulations of models of patients and imaging systems. DukeSim is a scanner‐specific CT imaging simulator that has previously been validated with simple cylindrical phantoms, but not with anthropomorphic conditions and clinically relevant measurements. Purpose To validate a scanner‐specific CT simulator (DukeSim) for the assessment of lung imaging biomarkers under clinically relevant conditions across multiple scanners using an anthropomorphic chest phantom, and to demonstrate the utility of virtual trials by studying the effects or radiation dose and reconstruction kernels on the lung imaging quantifications. Methods An anthropomorphic chest phantom with customized tube inserts was imaged with two commercial scanners (Siemens Force and Siemens Flash) at 28 dose and reconstruction conditions. A computational version of the chest phantom was used with a scanner‐specific CT simulator (DukeSim) to simulate virtual images corresponding to the settings of the real acquisitions. Lung imaging biomarkers were computed from both real and simulated CT images and quantitatively compared across all imaging conditions. The VIT framework was further utilized to investigate the effects of radiation dose (20–300 mAs) and reconstruction settings (Qr32f, Qr40f, and Qr69f reconstruction kernels using ADMIRE strength 3) on the accuracy of lung imaging biomarkers, compared against the ground‐truth values modeled in the computational chest phantom. Results The simulated CT images matched closely the real images for both scanners and all imaging conditions qualitatively and quantitatively, with the average biomarker percent error of 3.51% (range 0.002%–18.91%). The VIT study showed that sharper reconstruction kernels had lower accuracy with errors in mean lung HU of 84–94 HU, lung volume of 797–3785 cm3, and lung mass of −800 to 1751 g. Lower tube currents had the lower accuracy with errors in mean lung HU of 6–84 HU, lung volume of 66–3785 cm3, and lung mass of 170–1751 g. Other imaging biomarkers were consistent under the studied reconstruction settings and tube currents. Conclusion We comprehensively evaluated the realism of DukeSim in an anthropomorphic setup across a diverse range of imaging conditions. Th