(Sunday, 7/12/2020) 1:00 PM - 2:00 PM [Eastern Time (GMT-4)]
Room: Track 1
Purpose:
Monte Carlo simulation is the most accurate method for fetal dose evaluation from CT examinations. However, due to high computing power requirement and complicated simulation process, the method is limited to few research medical centers for a small number of patients. This study was to directly estimate radiation dose to the fetus for pregnant patients undergoing abdominopelvic CT examinations, without Monte Carlo simulation.
Methods:
This study considered 24 pregnant patient models (http://doi.org/10.5281/zenodo.2578337), each undergoing two abdominopelvic CT examinations with fixed mA and tube current modulation. Patient water equivalent diameter (WED) was 30.1±3.3 (25.3–35.6) cm [mean(range)]. Gestational age was <5 weeks for one patient, and 20.3±9.1 (5–35.9) weeks for the others. Fetal depth (from the anterior skin surface to the most anterior part of the fetus) was 6.1±2.1 (2.5–10.9) cm. In each patient examination, a previously reported method was used to evaluate cranial-caudal dose profile of a water phantom, with inputs of CTDIvol, mA and WED. Fetal average dose was calculated over the fetal volume in the cranial-caudal direction. Our fetal dose calculations were benchmarked with the previously reported values from Monte Carlo simulation (MCNPX version 2.7.a). Statistical software (R, version 3.5.1) was used to determine mean and 95th percentile.
Results:
The difference in magnitude between two approaches was (5.8±3.7)% (95th percentile, 10.8%) for 24 fixed-mA examinations, and (5.5±4.4)% (95th percentile, 12.4%) for 24 examinations with tube current modulation.
Conclusion:
The proposed fetal dose evaluation is suited for all stages of pregnancy, considering variations in maternal body size, fetus volume coverage, and fetus completely or partially inside or outside the scan range. The dose profile can facilitate graphical visualization of dose level across or beyond the fetus, which may provide a new tool for CT protocol optimization and radiation risk management.
Keywords
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Taxonomy
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