SU-F-P-19: Fetal Dose Estimate for a High-Dose Fluoroscopy Guided Intervention Using Modern Data Tools.


PURPOSE An accurate dose estimate is necessary for effective patient management after a fetal exposure. In the case of a high-dose exposure, it is critical to use all resources available in order to make the most accurate assessment of the fetal dose. This work will demonstrate a methodology for accurate fetal dose estimation using tools that have recently become available in many clinics, and show examples of best practices for collecting data and performing the fetal dose calculation. METHODS A fetal dose estimate calculation was performed using modern data collection tools to determine parameters for the calculation. The reference point air kerma as displayed by the fluoroscopic system was checked for accuracy. A cumulative dose incidence map and DICOM header mining were used to determine the displayed reference point air kerma. Corrections for attenuation caused by the patient table and pad were measured and applied in order to determine the peak skin dose. The position and depth of the fetus was determined by ultrasound imaging and consultation with a radiologist. The data collected was used to determine a normalized uterus dose from Monte Carlo simulation data. Fetal dose values from this process were compared to other accepted calculation methods. RESULTS An accurate high-dose fetal dose estimate was made. Comparison to accepted legacy methods were were within 35% of estimated values. CONCLUSION Modern data collection and reporting methods ease the process for estimation of fetal dose from interventional fluoroscopy exposures. Many aspects of the calculation can now be quantified rather than estimated, which should allow for a more accurate estimation of fetal dose.

DOI: 10.1118/1.4955726

Cite this paper

@article{Moirano2016SUFP19FD, title={SU-F-P-19: Fetal Dose Estimate for a High-Dose Fluoroscopy Guided Intervention Using Modern Data Tools.}, author={Jeff Moirano}, journal={Medical physics}, year={2016}, volume={43 6}, pages={3362} }