Abstract

Purpose: The purpose of this study was to extend the validation of Monte Carlo (MC) simulation based dose estimates by comparing simulated values with in‐vivo measurements from clinical patient scans. Methods: The Institutional Review Board approved the acquisition of in‐vivo rectal dose measurements in a pilot study of 9 patients undergoing CT Colonography on an MDCT (LightSpeed VCT, GE Healthcare). Per patient, two scans, prone and supine, were acquired both using a fixed mAs technique. In‐vivo dose measurements were obtained using TLD capsules that were affixed to the inner lumen of rectal catheters. Dose from the TLDs were determined taking into account their energy response. Simulations were performed using voxelized models of the patients and the TLDs based on CT image data. A previously developed MC based model was used to simulate the scanner and each patient scan. Dose to the TLD was estimated for each patient scan. The measured and simulated in‐vivo TLD dose values were compared for all 9 patients and the Root Mean Square (RMS) error was calculated as well as the correlation. Additionally the simulated results were compared with calculated Size Specific Dose Estimates (SSDEs) for each patient. Results: The RMS error between TLD measurements and MC simulations was 12.2% with a maximum of 20.3% and minimum of ‐ 19.7%. Comparison of simulations and SSDE resulted in an RMS of 17.0% with a minimum of ‐ 22.9% and maximum of 22.8%. Conclusion: The results of this study demonstrated that MC simulations using voxelized patient and equivalent source model Result in reasonably accurate doses compared to actual measured doses. Several possible improvements to the performance of the MC model were identified (including reconstructing images at the biggest field of view for full coverage of the anatomy, etc.) Dr. Michael McNitt‐Gray; Institutional research agreement, Siemens AG; Recipient research support Siemens AG; Consultant, Flaherty Sensabaugh Bonasso PLLC; Consultant, Fulbright and Jaworski, LLC; Maryam Khatonabadi and Kyle McMillan ripient research support Siemens AG

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