Advanced Skeletal Dosimetry across Pediatric and Adult Ages Using Mesh-Based Detailed Skeletal Models
Abstract
Purpose
The skeleton is of particular concern in radiation dosimetry, because it contains key target tissues for radiogenic cancers, including red bone marrow (RBM), associated with leukemia risk. However, skeletal dosimetry remains challenging due to the complex microstructure. To address this challenge, we recently developed 3D image-based detailed skeletal models in high-fidelity mesh format for eight age/sex groups spanning newborn to adult. In this study, these models were used to generate comprehensive datasets of electron and alpha specific absorbed fractions (SAFs) and photon fluence-to-dose response functions (DRFs).
Methods
This study used a total of 319 skeletal models representing diverse skeletal sites across all eight groups. These models, along with the ICRP-145/156 whole-body reference phantoms, were implemented in the PHITS Monte Carlo code (v3.35) to generate electron and alpha SAF datasets across all skeletal sites and groups. In addition, the resulting electron SAF datasets were combined with the NIST physical data library to derive new photon DRF datasets as an alternative to those of ICRP-116/155.
Results
SAF datasets were generated for electrons over 0.01–10 MeV and for alpha particles over 0.5–12 MeV for all skeletal sites and groups. Notably, this study provides the first pediatric SAF datasets calculated exclusively using 3D image-based skeletal models. Comparative analyses revealed pronounced age-dependent variations in SAFs, with differences reaching up to two orders of magnitude, primarily attributable to age-dependent differences in target tissue masses and anatomical geometry. In addition, DRF datasets applicable to photons over 0.01–10 MeV were generated.
Conclusion
SAF and DRF datasets generated in this study enable accurate and rapid skeletal dosimetry without requiring in-depth expertise in complex skeletal models or Monte Carlo techniques. Given that RBM is a key dose-limiting organ at risk in radiopharmaceutical therapy, these datasets are expected to contribute to safe and effective treatment delivery.