Share Email Print
cover

Proceedings Paper • new

A rapid GPU-based Monte Carlo simulation tool for individualized dose estimations in CT
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The rising awareness towards the risks associated with CT radiation has pushed forward the case for patient- specific dose estimation, one of the prerequisites for individualized monitoring and management of radiation exposure. The established technique of using Monte Carlo simulations to provide such dose estimates is computationally intensive, thus limiting their utility towards timely assessment of clinically relevant questions. To overcome this impediment, we have developed a rapid Monte Carlo simulation tool based on the MC-GPU frame- work for individualized dose estimation in CT. This tool utilizes the multi-threaded x-ray transport capability of MC-GPU, scanner-specific geometry and voxelized patient-specific models to produce realistic estimates of radiation dose. To demonstrate its utility, we utilized this tool to provide scanner-specific (LightSpeed VCT, GE Healthcare) organ dose estimates in abdominopelvic CT for a virtual population of 58 adult XCAT patient models. To gauge the accuracy of these estimates, the organ dose values from this new tool were compared against those from a previously published tool based on PENELOPE framework. The comparisons demonstrated the capability of our new simulation tool to produce dose estimates that agree with the published data within 5% for organs within primary field while simultaneously providing speedups as high as 70x over a CPU cluster-based execution model. This high accuracy of dose estimates coupled with the demonstrated speedup provides a viable model for rapid and personalized dose estimation.

Paper Details

Date Published: 9 March 2018
PDF: 9 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105733V (9 March 2018); doi: 10.1117/12.2294965
Show Author Affiliations
Shobhit Sharma, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Anuj Kapadia, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Ehsan Abadi, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Wanyi Fu, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
W. Paul Segars, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Ehsan Samei, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

© SPIE. Terms of Use
Back to Top