In adult abdomen-pelvis CT, equilibrium dose-pitch product better accounts for the kVp dependence of organ dose than CTDI100.

Purpose: Monte Carlo simulation is a frequently used technique for assessing patient dose in CT. The accuracy of a Monte Carlo program is often validated using the standard CT dose index (CTDI) phantoms by comparing simulated and measured CTDI100. To achieve good agreement, many input parameters in the simulation (e.g., energy spectrum and effective beam width) need to be determined. However, not all the parameters have equal importance. Our aim was to assess the relative importance of the various factors that influence the accuracy of simulated CTDI100. Methods: A Monte Carlo program previously validated for a clinical CT system was used to simulate CTDI100. For the standard CTDI phantoms (32 and 16 cm in diameter), CTDI100 values from central and four peripheral locations at 70 and 120 kVp were first simulated using a set of reference input parameter values (treated as the truth). To emulate the situation in which the input parameter values used by the researcher may deviate from the truth, additional simulations were performed in which intentional errors were introduced into the input parameters, the effects of which on simulated CTDI100 were analyzed. Results: At 38.4‐mm collimation, errors in effective beam width up to 5.0 mm showed negligible effects on simulated CTDI100 (<1.0%). Likewise, errors in acrylic density of up to 0.01 g/cm3 resulted in small CTDI100 errors (<2.5%). In contrast, errors in spectral HVL produced more significant effects: slight deviations (±0.2 mm Al) produced errors up to 4.4%, whereas more extreme deviations (±1.4 mm Al) produced errors as high as 25.9%. Lastly, ignoring the CT table introduced errors up to 13.9%. Conclusion: Monte Carlo simulated CTDI100 is insensitive to errors in effective beam width and acrylic density. However, they are sensitive to errors in spectral HVL. To obtain accurate results, the CT table should not be ignored. This work was supported by a Faculty Research and Development Award from Cleveland State University.

Purpose: In Monte Carlo simulations of patient dose from a CT scan, accurate knowledge about the x‐ray energy spectra is essential. Simulated CT dose index (CTDI₁₀₀) free‐in‐air is often used together with measured values to calibrate the intensity of the spectra. To simulate CTDI₁₀₀, a computational model of the ion chamber is required. Various chamber modeling methods have been reported. The purpose of this study was to investigate systematically how chamber modeling affects simulated dose response. Both the pencil chamber and the thimble chamber recently recommended by AAPM TG111 were studied. Methods: A Monte Carlo program previously validated for a clinical CT system (SOMATOM Definition Flash, Siemens Healthcare) was used. To examine the effect of chamber modeling, a highly detailed model of an actual CTDI₁₀₀ pencil chamber (model 10×5−3CT, Radcal Corporation) was first defined. Five additional models with reduced detail were then created. They differed from the first model in terms of their dimensions, component parts, material composition, and the use of quadric or voxel geometry. Two models of a TG111 thimble chamber were also created, representing a highly detailed and a rather crude rendition of an actual thimble chamber (model 10×0.6−3CT, Radcal Corporation). Single axial scans for CTDI₁₀₀ chamber models and helical scans for TG111 chamber models were simulated free‐in‐air at 70 and 120 kVp. Simulated dose responses were then compared amongst different chamber models. Results: For the six models of the CTDI₁₀₀ pencil chamber, the coefficient of variation of the simulated CTDI₁₀₀ was 0.9% at 70 kVp and 1.0% at 120 kVp. For the two models of the TG111 thimble chamber, the difference in the simulated equilibrium‐dose‐pitch product was 1.9% at 70 kVp and 2.1% at 120 kVp. Conclusion: In Monte Carlo simulations of CT ion chambers, detailed modeling of chamber geometry and material composition is not necessary. This research is supported in part by a Faculty Startup Fund from Cleveland State University.