Objective: A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for accurate prediction of absorbed doses per organ prior to the imaging acquisition considering only personalized anatomical characteristics of any new pediatric patient. Approach: GATE Monte Carlo simulations were performed using a population of computational pediatric models to calculate the specific absorbed dose rates (SADRs) in several organs. A simulated dosimetry database was developed for 28 pediatric phantoms (age range 2–17 years old, both genders) and 5 different radiopharmaceuticals. Machine Learning regression models were trained on the produced simulated dataset, with leave one out cross validation for the prediction model evaluation. Hyperparameter optimization and ensemble learning techniques for a variation of input features were applied for achieving the best predictive power, leading to the development of a SADR prediction toolkit for any new pediatric patient for the studied organs and radiopharmaceuticals. Main results . SADR values for 30 organs of interest were calculated via Monte Carlo simulations for 28 pediatric phantoms for the cases of five radiopharmaceuticals. The relative percentage uncertainty in the extracted dose values per organ was lower than 2.7%. An internal dosimetry prediction toolkit which can accurately predict SADRs in 30 organs for five different radiopharmaceuticals, with mean absolute percentage error on the level of 8% was developed, with specific focus on pediatric patients, by using Machine Learning regression algorithms, Single or Multiple organ training and Artificial Intelligence ensemble techniques. Significance: A large simulated dosimetry database was developed and utilized for the training of Machine Learning models. The developed predictive models provide very fast results (90% with respect to the ground truth of Monte Carlo, considering personalized anatomical characteristics and the biodistribution of each radiopharmaceutical. The proposed method is applicable to other medical dosimetry applications in different patients’ populations.

Background/Objectives: Lutetium-177 is a widely used radioisotope in targeted radionuclide therapy, particularly for treating certain types of cancers relying on beta and low-energy gamma emissions, making it suitable for both therapeutic and post-therapy monitoring purposes. The purpose of this study was to evaluate the technical parameters for developing a prototype portable gamma camera dedicated to ¹⁷⁷Lu imaging applications. Methods: The well-validated GATE Monte Carlo toolkit was used to study the characteristics of the system and evaluate its performance in terms of spatial resolution, sensitivity, and image quality. For this purpose, a series of Monte Carlo simulations were executed, modeling a channel-edge aperture pinhole collimator incorporating a variety of computational phantoms. The final configuration of the prototype was standardized, incorporating the crystal size, collimator design, shielding, and the optimal FOV. After the development of the actual prototype camera, the system was also validated experimentally on the same setups as the simulations. Results: The final configuration of the prototype imaging system was standardized based on simulation results and then experimentally validated using physical phantoms under equivalent conditions. A minification of 1:5, spatial resolution of 1.0 cm, and sensitivity of 5.2 Cps/MBq at 10 cm distance source-to-collimator distance were assessed and confirmed. The experimental results agreed within 5% of simulated values. Conclusions: This study establishes the technical feasibility and foundational performance of a portable pinhole imaging system for potential clinical use in ¹⁷⁷Lu imaging workflows and thereby improving therapeutic effectiveness.