Patient-specific fetal radiation dosimetry for pregnant patients undergoing abdominal and pelvic CT imaging

Background: Accurate estimation of fetal radiation dose is crucial for risk-benefit analysis of radiological imaging, while the radiation dosimetry studies based on individual pregnant patient are highly desired. Purpose: To use Monte Carlo calculations for estimation of fetal radiation dose from abdominal and pelvic computed tomography (CT) examinations for a population of patients with a range of variations in patients’ anatomy, abdominal circumference, gestational age (GA), fetal depth (FD), and fetal development. Methods: Forty-four patient-specific pregnant female models were constructed based on CT imaging data of pregnant patients, with gestational ages ranging from 8 to 35 weeks. The simulation of abdominal and pelvic helical CT examinations was performed on three validated commercial scanner systems to calculate organ-level fetal radiation dose. Results: The absorbed radiation dose to the fetus ranged between 0.97 and 2.24 mGy, with an average of 1.63 ± 0.33 mGy. The CTDIvol-normalized fetal dose ranged between 0.56 and 1.30, with an average of 0.94 ± 0.25. The normalized fetal organ dose showed significant correlations with gestational age, maternal abdominal circumference (MAC), and fetal depth. The use of ATCM technique increased the fetal radiation dose in some patients. Conclusion: A technique enabling the calculation of organ-level radiation dose to the fetus was developed from models of actual anatomy representing a range of gestational age, maternal size, and fetal position. The developed maternal and fetal models provide a basis for reliable and accurate radiation dose estimation to fetal organs.</p

Absorbed dose differences between twin fetuses for pregnancy patients in CT examinations

Background: Estimation of the radiation dose to the fetus is essential for the assessment of radiation risks and benefits to pregnant patients undergoing radiological examinations. During the past decade, the global twinning rate has soared resulting from embryo assistance and increased delivery age. However, to the best of our knowledge, radiation dosimetry in multiple pregnancies from radiological imaging has never been reported before. Purpose: The purpose of this study is to develop personalized computational models for twin fetuses based on clinical CT images of real pregnant patients and to estimate personalized radiation doses for twin fetuses from abdominal/pelvic CT examinations. Methods: Personalized computational phantoms representing pregnant females with twins at the second and third trimesters were constructed based on CT images of two pregnant patients. Monte Carlo calculations were performed using the MCNP transport code and three validated CT scanners to estimate the radiation dose of twin fetuses during abdominal and pelvic CT examinations. Results: The absorbed fetal organ dose was calculated and compared between twins. For the same patient, the absolute difference in fetal organ dose between twins varies between 0.63% and 39.64% with an average value of 12.85%. The estimated total-body dose differences for twin fetuses were 11.55% and 7.51%, respectively, for pregnant patients at 22 and 30 weeks gestational age. Conclusion: The variations of body weight and organ mass affect the absorbed dose of twin fetuses. Personalized computational models provide more accurate fetal radiation dosimetry estimates for pregnant patients with twins. This work also contributes to a better understanding of model-induced uncertainties in external radiation dosimetry for the developing fetus.</p

Optimizing dosimetry in Y-90 microsphere radioembolization: GPU-accelerated Monte Carlo simulation versus conventional methods for high-volume setting

Abstract Background Yttrium-90 (90Y) microsphere radioembolization has shown unique advantages in treating both primary and metastatic liver cancer and was introduced into China in 2022. Despite the development of various dosimetric models—ranging from empirical to voxel-based approaches—practical implementation remains challenging. With over 370,000 new liver cancer cases annually and limited access to certified 90Y treatment centers, Chinese interventional oncology departments face increasing pressure to balance dosimetric accuracy with clinical efficiency. This study aims to develop a GPU-based fast Monte Carlo project for accurate voxel-level dose calculation and to evaluate its performance alongside existing dosimetric strategies, with the goal of supporting optimized clinical workflows in high-volume settings. Methods A fast Monte Carlo simulation algorithm was developed using Graphics Processing Unit (GPU) acceleration and applied retrospectively to eight patients diagnosed with hepatocellular carcinoma or metastatic colorectal cancer. The dosimetric performance of the GPU-based approach was compared against direct Monte Carlo (MC) simulations, the Medical Internal Radiation Dose (MIRD) formalism, the Voxel S-value (VSV) method, and the Local Energy Deposition (LED) model. Voxel- and organ-level dose accuracy were quantified using metrics such as Mean Absolute Relative Error (MARE), Relative Standard Deviation (RSD), and D95 in dose volume histogram. Statistical comparisons were conducted using Shapiro-Wilk normality tests and repeated measures ANOVA to assess inter-method differences. Results The GPU-based Monte Carlo code demonstrated high accuracy and computational efficiency. Using direct MC simulation as the reference, the GPU-based approach yielded the lowest voxel-level variability, with median RSDs in high-activity transverse regions reaching − 1.13%, indicating superior consistency. Corresponding MARE were 4.53% for the GPU method, compared to 6.71% for VSV and 49.36% for LED, confirming its dosimetric reliability. At the organ level, the GPU-based method achieved RSDs of 0.35% ± 0.80% (tumor), -0.45% ± 0.76% (liver), 1.41% ± 4.45% (lung), and − 1.43% ± 1.23% (spleen), significantly outperforming alternative models. Notably, VSV and LED substantially underestimated lung dose (-52.19% ± 23.87%, -53.71 ± 22.17%), highlighting their limited applicability in heterogeneous regions. In contrast, the dose of spleen (F = 3.26, p = 0.069) and kidneys (F = 3.22, p = 0.071) did not show statistically significant differences between methods. In terms of computational performance, the GPU-based code delivered a remarkable 1,296-fold speed-up over traditional MC simulations, enabling efficient voxel-level dosimetry suitable for clinical workflows. Conclusion The GPU-based fast Monte Carlo simulation provides a highly accurate and computationally efficient tool for voxel-level dosimetry in 90Y radioembolization. It enables precise estimation of tumor and lung doses with significantly reduced processing time and hardware demands, offering clear clinical advantages in minimizing radiation pneumonitis risk and supporting high-throughput workflows. Importantly, a stratified approach to dosimetric modeling—selecting simplified methods such as VSV or LED for small, well-contained lesions, and reserving GPU-based Monte Carlo for anatomically complex or heterogeneous cases—may optimize the balance between accuracy and efficiency. Future work will focus on large-scale validation and formalizing model selection criteria tailored to tumor morphology and treatment scope, with the aim of advancing personalized dosimetric planning in liver-directed therapies. Clinical trial number Not applicable