BackgroundComputed tomography (CT) angiography (CTA) is a non‐invasive imaging method used to detect arteries and examine various brain diseases. When CTA is performed for follow‐up or postoperative evaluation, reproducibility of vessel delineation is required. A reproducible and stable contrast enhancement can be achieved by manipulating the factors affecting it. Previous studies have investigated several factors that alter the contrast enhancement of arteries. However, no reports establishing the effect of different operators on contrast enhancement exist.PurposeTo assess the differences between inter‐operator arterial contrast enhancement in cerebral CTA using Bayesian statistical modeling.MethodsImage data were obtained using a multistage sampling method from the cerebral CTA scans of patients who underwent the process between January 2015 and December 2018. Several Bayesian statistical models were developed, and the objective variable was the mean CT number of the bilateral internal carotid arteries after contrast enhancement. The explanatory variables were sex, age, fractional dose (FD), and the operator's information. The posterior distributions of the parameters were computed via Bayesian inference using the Markov chain Monte Carlo (MCMC) method, with the Hamiltonian Monte Carlo method employed as the algorithm. The posterior predictive distributions were computed using the posterior distributions of the parameters. Finally, the differences between inter‐operator arterial contrast enhancement on the CT number in cerebral CTA were estimated.ResultsThe posterior distributions showed that all parameters representing the difference between operators included zero at the 95% credible intervals (CIs). The maximum mean difference between inter‐operator CT number in the posterior predictive distribution was only 12.59 Hounsfield units (HUs).ConclusionsThe Bayesian statistical modeling results suggest that contrast enhancement of cerebral CTA examination between operator‐to‐operator differences in postcontrast CT number was small compared to those within‐operator differences resulting from factors not considered in the model.