A rapid GPU-based Monte Carlo simulation tool for individualized dose estimations in CT

Shobhit Sharma; Anuj Kapadia; Ehsan Abadi; Wanyi Fu; W. Paul Segars; Ehsan Samei

doi:10.1117/12.2294965

A rapid GPU-based Monte Carlo simulation tool for individualized dose estimations in CT

Shobhit Sharma, Anuj Kapadia, Ehsan Abadi, Wanyi Fu, W. Paul Segars, Ehsan Samei

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

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.

Original language	English
Title of host publication	Medical Imaging 2018
Subtitle of host publication	Physics of Medical Imaging
Editors	Taly Gilat Schmidt, Guang-Hong Chen, Joseph Y. Lo
Publisher	SPIE
ISBN (Electronic)	9781510616356
DOIs	https://doi.org/10.1117/12.2294965
State	Published - 2018
Externally published	Yes
Event	Medical Imaging 2018: Physics of Medical Imaging - Houston, United States Duration: Feb 12 2018 → Feb 15 2018

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	10573
ISSN (Print)	1605-7422

Conference

Conference	Medical Imaging 2018: Physics of Medical Imaging
Country/Territory	United States
City	Houston
Period	02/12/18 → 02/15/18

Funding

The research reported in this document was supported by the National Institutes of Health under award numbers 2R01EB001838-09A1 and HL131753. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce GTX Titan X GPU used for this research.

Funders	Funder number
National Institutes of Health	2R01EB001838-09A1, HL131753
NVIDIA

Keywords

GPU
Monte Carlo
Organ Dose
Patient Specific
Rapid

Access to Document

10.1117/12.2294965

Cite this

Sharma, S., Kapadia, A., Abadi, E., Fu, W., Segars, W. P., & Samei, E. (2018). A rapid GPU-based Monte Carlo simulation tool for individualized dose estimations in CT. In T. G. Schmidt, G.-H. Chen, & J. Y. Lo (Eds.), Medical Imaging 2018: Physics of Medical Imaging Article 105733V (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10573). SPIE. https://doi.org/10.1117/12.2294965

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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.",

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author = "Shobhit Sharma and Anuj Kapadia and Ehsan Abadi and Wanyi Fu and Segars, {W. Paul} and Ehsan Samei",

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Sharma, S, Kapadia, A, Abadi, E, Fu, W, Segars, WP & Samei, E 2018, A rapid GPU-based Monte Carlo simulation tool for individualized dose estimations in CT. in TG Schmidt, G-H Chen & JY Lo (eds), Medical Imaging 2018: Physics of Medical Imaging., 105733V, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10573, SPIE, Medical Imaging 2018: Physics of Medical Imaging, Houston, United States, 02/12/18. https://doi.org/10.1117/12.2294965

A rapid GPU-based Monte Carlo simulation tool for individualized dose estimations in CT. / Sharma, Shobhit; Kapadia, Anuj; Abadi, Ehsan et al.
Medical Imaging 2018: Physics of Medical Imaging. ed. / Taly Gilat Schmidt; Guang-Hong Chen; Joseph Y. Lo. SPIE, 2018. 105733V (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10573).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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A rapid GPU-based Monte Carlo simulation tool for individualized dose estimations in CT

Abstract

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Funding

Keywords

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