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Creation of a Set of Reference Data for Organ Doses Derived From Monte Carlo Simulations of CT Exams

A Hardy1*, M Bostani1 , E Angel2 , C Cagnon1 , M McNitt-Gray1 , (1) UCLA, Los Angeles, CA, (2) Canon Medical Systems, Tustin, CA


(Tuesday, 7/31/2018) 4:30 PM - 6:00 PM

Room: Room 202

Purpose: AAPM Report 195 describes reference datasets that provide information necessary for direct comparison of results between different Monte Carlo simulation tools. The purpose of this work was to extend that effort by providing reference datasets that will allow for estimation of absolute and normalized organ doses from CT exams.

Methods: The proposed reference datasets contain scanner, patient and exam characteristics as well as the resulting organ doses from a reference set of simulations. Scanner characteristics include descriptions of equivalent source spectrum and bowtie filtration profile, as well as scanner geometry information. Normalization factors are also provided which supply the information necessary to convert MC simulation results to absolute dose values. The patient models used are publicly available fetal dose models that contain image data and voxelized MC input files with fetus, uterus, and gestational sac location information as well as patient size metrics (water equivalent diameter - Dw). Exam characteristics such as scan length and imaging protocol specifications are provided. For simulations involving tube current modulation (TCM), an estimate of TCM will be given based upon a validated method that accounts for patient attenuation and scanner tube current limitations. For each patient model and CT exam scenario, both the absolute and CTDIvol-normalized fetal dose estimate is provided from a reference set of simulations.

Results: For benchmarking purposes, results of absolute and normalized fetal dose will be presented in tabular form with associated MC error estimates. In addition, CTDIvol estimates based on average tube current across the scan volume.

Conclusion: A reference dataset for MC benchmarking is provided. This provides researchers with an opportunity to compare their simulations to a set of reference data and to validate fetal dose estimations from MC.

Funding Support, Disclosures, and Conflict of Interest: Michael McNitt-Gray, Ph.D Departmental master research agreement, Siemens Healthineers Erin Angel, Ph.D Employee of Canon Medical Systems


Monte Carlo, Phantoms, Dosimetry


IM- CT: Phantoms - digital

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