  Considering the increasing trend of physical examinations in China, the
escalating frequency of Computed Tomography (CT) scans has amplified concerns
regarding population radiation exposure and its consequent risks. The
challenges mainly manifest in two aspects: one is the rapid construction of
patient-specific human phantoms, and the other is the fast Monte Carlo (MC)
simulation of radiation dose. Hence, this study aims to demonstrate a near
real-time MC patient-specific organ dose computation method, for the first
time, involving automatic segmentation across a large dataset of 11,482
subjects undergoing chest CT scans. We developed a preliminary software
platform, integrating the automatic segmentation software DeepViewer and the
GPU-accelerated MC engine ARCHER-CT. Comparisons with traditional dosimetry
methods revealed up to 100% discrepancies for a few subjects in organ-level
dose, underscoring the patient-specific method's superior accuracy. This study
paves the way for more accurate radiation risk assessment, crucial in the era
of patient-specific radiation dosimetry.
