The accuracy of computed tomography number to electron density (CT‐ED) calibration is a key component for dose calculations in an inhomogeneous medium. In a previous work, it was shown that the tolerance levels of CT‐ED calibration became stricter with an increase in tissue thickness and decrease in the effective energy of a photon beam. For the last decade, a low effective energy photon beam (e.g., flattening‐filter‐free (FFF)) has been used in clinical sites. However, its tolerance level has not been established yet. We established a relative electron density (ED) tolerance level for each tissue type with an FFF beam. The tolerance levels were calculated using the tissue maximum ratio (TMR) and each corresponding maximum tissue thickness. To determine the relative ED tolerance level, TMR data from a Varian accelerator and the adult reference computational phantom data in the International Commission on Radiological Protection publication 110 (ICRP‐110 phantom) were used in this study. The 52 tissue components of the ICRP‐110 phantom were classified by mass density as five tissues groups including lung, adipose/muscle, cartilage/spongy‐bone, cortical bone, and tooth tissue. In addition, the relative ED tolerance level of each tissue group was calculated when the relative dose error to local dose reached 2%. The relative ED tolerances of a 6 MVFFF beam for lung, adipose/muscle, and cartilage/spongy‐bone were ±0.044, ±0.022, and ±0.044, respectively. The thicknesses of the cortical bone and tooth groups were too small to define the tolerance levels. Because the tolerance levels of CT‐ED calibration are stricter with a decrease in the effective energy of the photon beam, the tolerance levels are determined by the lowest effective energy in useable beams for radiotherapy treatment planning systems.

Computed tomography (CT) data are required to calculate the dose distribution in a patient’s body. Generally, there are two CT number calibration methods for commercial radiotherapy treatment planning system (RTPS), namely CT number‐relative electron density calibration (CT‐RED calibration) and CT number‐mass density calibration (CT‐MD calibration). In a previous study, the tolerance levels of CT‐RED calibration were established for each tissue type. The tolerance levels were established when the relative dose error to local dose reached 2%. However, the tolerance levels of CT‐MD calibration are not established yet. We established the tolerance levels of CT‐MD calibration based on the tolerance levels of CT‐RED calibration. In order to convert mass density (MD) to relative electron density (RED), the conversion factors were determined with adult reference computational phantom data available in the International Commission on Radiological Protection publication 110 (ICRP‐110). In order to validate the practicability of the conversion factor, the relative dose error and the dose linearity were validated with multiple RTPSes and dose calculation algorithms for two groups, namely, CT‐RED calibration and CT‐MD calibration. The tolerance levels of CT‐MD calibration were determined from the tolerance levels of CT‐RED calibration with conversion factors. The converted RED from MD was compared with actual RED calculated from ICRP‐110. The conversion error was within ±0.01 for most standard organs. It was assumed that the conversion error was sufficiently small. The relative dose error difference for two groups was less than 0.3% for each tissue type. Therefore, the tolerance levels for CT‐MD calibration were determined from the tolerance levels of CT‐RED calibration with the conversion factors. The MD tolerance levels for lung, adipose/muscle, and cartilage/spongy‐bone corresponded to ±0.044, ±0.022, and ±0.045 g/cm3, respectively. The tolerance levels were useful in terms of approving the CT‐MD calibration table for clinical use.

number. We established stoichiometric CT number calibration using only two materials because the accuracy of the process was determined not by the number of used materials but by the number of elements contained. The stoichiometric CT number calibration was comparable to the tissue-substitute calibration, with a dose difference less than 1%. An outline of the CT number calibration audit was demonstrated through a multi-institutional study. Conclusions: We established a new stoichiometric CT number calibration method for validating the CT number calibration tables registered in RTPSs. We also developed a CT number calibration phantom for a postal audit, which was verified by the performances of multiple CT scanners located at several institutions. The new stoichiometric CT number calibration has the advantages of being performed using only two materials, and decreasing the difference between the calculated and measured CT numbers for air and lung tissue. In the future, a postal CT number calibration audit might be achievable using a smaller phantom.

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We analyzed the predictive error in 4D modeling and the error due to the amplitude and period of target. 4D modeling error substantially increased with increasing amplitude and decreasing period of the target motion.