Assessment of uncertainties associated with Monte Carlo-based personalized dosimetry in clinical CT examinations

The clinical value of x-ray computed tomography (CT) has skyrocketed in the last decade while at the same time being the main source of medical exposure to the population. Concerns regarding the potential health hazards associated with the use of ionizing radiation were raised and an appropriate estimation of absorbed dose to patients is highly desired. In this work, we aim to validate our developed Monte Carlo CT simulator using in-phantom dose measurements and further assess the impact of personalized scan-related parameters on dosimetric calculations. We developed a Monte Carlo-based CT simulator for personalized organ level dose calculations, in which the CT source model, patient-specific computational model and personalized scanning protocol were integrated. The CT simulator was benchmarked using an ionization chamber and standard CT Dose Index phantom while the dosimetry methodology was validated through experimental measurements using thermoluminescent dosimeters (TLDs) embedded within an anthropomorphic phantom. Patient-specific scan protocols extracted from CT raw data and DICOM image metadata, respectively, were fed as input into the CT simulator to calculate individualized dose profiles. Thereby, the dosimetric uncertainties associated with using different protocol-related parameters were investigated. The absolute absorbed dose difference between measurements and simulations using the ionization chamber was less than 3%. In the case of the anthropomorphic phantom, the absolute absorbed dose difference between simulations and TLD measurements ranged from -8.3% to 22%, with a mean absolute difference of 14% while the uncertainties of protocol-related input parameters introduced an extra absolute error of 15% to the simulated results compared with TLD measurements. The developed methodology can be employed for accurate estimation of organ level dose from clinical CT examinations. The validated methodology can be further developed to produce an accurate MC simulation model with a reduced computational burden.