Construction of personalized computational phantoms of pregnant patients for assessment of CT radiation dose

Abstract

The use of x-ray computed tomography (CT) has increased drastically worldwide during the last two decades and is nowadays playing a pivotal role in clinical diagnosis and radiation therapy planning. The radiation dose delivered to the fetus during CT imaging procedures of pregnant patients raised fhealth concerns because of the high radiosensitivity of the developing embryo/fetus. To assess the diagnostic benefits considering the radiation risks, the radiologist needs reasonably accurate and detailed estimates of the fetal dose from diagnostic CT imaging protocols. To produce realistic biological and physical representations of pregnant patients and embedded fetus, we developed a methodology for construction of patient-specific voxel-based computational phantoms based on existing standardized hybrid computational pregnant female phantoms. We estimated the maternal absorbed dose and fetal organ dose for pregnant patients referred to the emergency unit of Geneva University Hospital for abdominal CT scans. The N-Particle eXtended (MCNPX) general purpose Monte Carlo code was used for radiation transport simulation. The obtained results were compared to those reported by RadimetricsTM (Bayer Healthcare) dose tracking software. The estimated effective dose to the mother varied from 1.1 mSv to 2.0 mSv with an average of 1.6 mSv while RadimetricsTM commercial dose tracking software reported an average effective dose of 1.9 mSv. The normalized fetal dose is about 9.2 mGy/100mAs. The fetus brain and bone marrow receive an average dose of 3.11 ± 1.09 mGy and 1.44 ± 0.45 mGy, respectively. The methodology developed for construction of personalized computational models can be exploited to estimate patient-specific radiation dose from CT imaging procedures. The generated dosimetric data can be used for accurate assessment of the radiation risks to the pregnant patient and fetus from various CT scanning protocols, thus guiding the decision making process.