Treffer: Fast personalized CT dose calculations with GPUMCD.

Purpose: The continuous increase of population dose due to ever-rising Computed Tomography (CT) examinations has called for more personalized dose estimations in medical imaging - a far from trivial task. This study aims to demonstrate a GPU-enabled pipeline combining automatic segmentation with GPU Monte Carlo Dose (GPUMCD) simulations to provide patient-specific dose-to-organ CT dosimetry reports using existing patient CT images.
Methods: A dynamic representation of the CT imaging process was reproduced within GPUMCD using information in DICOM headers, complemented by in-house exposure measurements, and validated in homogeneous and anthropomorphic phantoms. A dose pipeline was implemented using GPUMCD and a pre-trained open-source nnU-net model (TotalSegmentator). Dose-to-organ dosimetry was obtained for images from a lung cancer screening program and stored in DICOM-compliant Structured Reports.
Results: GPUMCD calculated dose values were within 5.5% of measurements for all phantoms and investigated conditions. Utilizing one A100-SXM4-40GB GPU, the average pipeline runtime was 6 min and 06 s per CT study. The GPU-driven simulation and segmentation operation took 46% (2 min and 7 s) of the total runtime, and data processing (file reading, conversion, and writing) occupied the remaining 54%.
Conclusion: This work demonstrates the ability to generate patient-specific three-dimensional dose distributions in CT within a few seconds and the subsequent feasibility of performing fully automated mass personalized dose-to-organ calculations. The pipeline ingests and produces DICOM-compliant data compatible with clinical and research environments, enabling routine imaging dosimetry and large-scale retroactive dosimetry studies.
(Copyright © 2025 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.