Patient-Specific CT Doses Using DL-based Image Segmentation and GPU-based Monte Carlo Calculations for 10,281 Subjects

Abstract

Computed tomography (CT) scans are a major source of medical radiation exposure worldwide. In countries like China, the frequency of CT scans has grown rapidly, particularly in routine physical examinations where chest CT scans are increasingly common. Accurate estimation of organ doses is crucial for assessing radiation risk and optimizing imaging protocols. However, traditional methods face challenges due to the labor-intensive process of manual organ segmentation and the computational demands of Monte Carlo (MC) dose calculations. In this study, we present a novel method that combines automatic image segmentation with GPU-accelerated MC simulations to compute patient-specific organ doses for a large cohort of 10,281 individuals undergoing CT examinations for physical examinations at a Chinese hospital. This is the first big-data study of its kind involving such a large population for CT dosimetry. The results show considerable inter-individual variability in CTDIvol-normalized organ doses, even among subjects with similar BMI or WED. Patient-specific organ doses vary widely, ranging from 33% to 164% normalized by the doses from ICRP Adult Reference Phantoms. Statistical analyses indicate that the "Reference Man" based average phantoms can lead to significant dosimetric uncertainties, with relative errors exceeding 50% in some cases. These findings underscore the fact that previous assessments of radiation risk may be inaccurate. It took our computational tool, on average, 135 seconds per subject, using a single NVIDIA RTX 3080 GPU card. The big-data analysis provides interesting data for improving CT dosimetry and risk assessment by avoiding uncertainties that were neglected in the past.