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

Abstract

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. This study paves the way toward utilizing VITs more reliably for conducting medical imaging experiments that are not practical using actual patient images.

A systematic assessment and optimization of photon‐counting CT for lung density quantifications

“…The reliability of such evaluations may be affected due to the presence of bias across the modeled scanners. To mitigate this concern and ensure the validity of our evaluations, DukeSim has been previously validated against experimental measurements, demonstrating consistent validation accuracy across imaging parameters of the PCCT (Figure S2 22 ) and EICT 23,24 scanner models. Table S2 exemplifies the minimal existent bias across scanner models using quantification of lung density for one virtual patient.…”

“…4 The virtual human models were scanned using a scanner-specific CT simulator (DukeSim), 18,19 which takes as input a virtual subject and information about the scanner and protocol to generate CT projection images using a hybrid integration of ray-tracing and Monte Carlo (MC) techniques. 20,21 DukeSim has been previously validated against experimental measurements across scanner models (PCCT 22 and EICT 23,24 ) and imaging parameters with consistent validation accuracy. [22][23][24] The validation ensures that minimal bias exists across the modeled scanners, enabling reliable comparison between the scanners under different imaging parameters.…”

“…20,21 DukeSim has been previously validated against experimental measurements across scanner models (PCCT 22 and EICT 23,24 ) and imaging parameters with consistent validation accuracy. [22][23][24] The validation ensures that minimal bias exists across the modeled scanners, enabling reliable comparison between the scanners under different imaging parameters.…”

Development and application of a virtual imaging trial framework for airway quantifications via CT

“…The DukeSim CT simulator was used to image the human phantoms, by generating scanner-specific CT projection images of voxelized computational phantoms that take into account the physical and geometric characteristics of the scanners [16][17][18][19][20]. In this study, a clinical PCCT and an energy-integrating CT (EICT) (NAEOTOM Alpha, SOMATOM Definition Flash, Siemens) were simulated by DukeSim to generate the CT projections of the phantoms.…”

A systematic assessment of photon-counting CT for bone mineral density and microarchitecture quantifications

“…Clinical data is currently limited and costly for PCCT scanners, and it is not ethically viable to acquire multiple scans of patients for protocol optimization. Alternative to use of clinical data, virtual imaging trials (VITs) [5], [6] can be used to assess the potential of high resolution PCCT scanning for bone imaging applications. VITs allow for multiple acquisitions on the same subject to efficiently investigate the accuracy of current biomarkers, as well as microarchitecture in bone imaging.…”