Fetal Organ-level Specific Absorbed Fractions from Maternal Photon Sources Using Pregnant Female Phantoms

利用孕妇模型测定母体光子源对胎儿器官水平的特定吸收分数

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Abstract

Accurate estimation of fetal radiation dose is essential for protecting developing fetuses in maternal exposure scenarios. Current tools lack the anatomical detail required for fetal organ-level dosimetry, which is critical for radiation protection and organ-specific risk assessment. We developed a comprehensive dataset of photon specific absorbed fractions (SAFs) from maternal source to fetal target regions, calculated using anatomically realistic hybrid pregnant female phantoms with detailed fetal models representing eight gestational ages (8-38 weeks). The dataset covers 70 maternal source regions and 55 fetal target regions, providing the most detailed fetal organ dosimetry to date. Chord length distributions were analyzed to characterize spatial relationships between maternal and fetal organs. SAFs strongly depended on anatomical proximity: maternal organs adjacent to the fetus (placenta, amniotic fluid, urinary bladder wall) yielded high SAFs (>10(-1) kg(-1)) even at low photon energies, while distant organs (thyroid, liver) produced lower values (<10(-2) kg(-1)). Distinct temporal patterns emerged: SAFs from distant sources increased with gestational age, whereas those from adjacent organs decreased due to fetal mass growth. SAFs also varied markedly across fetal organs, reflecting complex maternal-fetal anatomical relationships. In some cases, fetal total body SAFs differed substantially from individual organ SAFs, highlighting the importance of organ-specific estimates in internal dosimetry. Compared to existing datasets, our SAFs offer improved anatomical realism, continuous gestational coverage, and detailed fetal target specification. These enhancements support more accurate fetal organ-level dose assessments for applications in clinical decision-making, epidemiological studies, and the development of radiation protection guidelines for pregnant populations.

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