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Proceedings Paper

A comprehensive GPU-based framework for scatter estimation in single source, dual source, and photon-counting CT
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Paper Abstract

Scattered radiation is one of the leading causes of image quality degradation in computed tomography (CT), leading to decreased contrast sensitivity and inaccuracy of CT numbers. The established gold-standard technique for scatter estimation in CT is Monte Carlo (MC) simulation, which is computationally expensive, thus limiting its utility for clinical applications. In addition, the existing MC tools are generalized and often do not model a realistic patient and/or a scanner-specific scenario, including lack of models for alternative CT configurations. This study aims to fill these gaps by introducing a comprehensive GPU-based MC framework for estimating patient and scanner-specific scatter for single-source, dual-source, and photon-counting CT using vendor-specific geometry/components and anatomically realistic XCAT phantoms. The tool accurately models the physics of photon transport and includes realistic vendor-specific models for x-ray spectra, bowtie filter, anti-scatter grid, and detector response. To demonstrate the functionality of the framework, we characterized the scatter profiles for a Mercury and an XCAT phantom using multiple scanner configurations. The timing information from the simulations was tallied to estimate the speedup over a comparable CPU-based MC tool. We also utilized the scatter profiles from the simulations to enhance the realism of primary-only ray-traced images generated for the purpose of virtual clinical trials (VCT). A speedup as high as 900x over a CPU-based MC tool was also observed for our framework. The results indicate the capability of this framework to quantify scatter for different proposed CT configurations and the significance of scatter contribution for simulating realistic CT images.

Paper Details

Date Published: 1 March 2019
PDF: 10 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109482V (1 March 2019); doi: 10.1117/12.2513198
Show Author Affiliations
Shobhit Sharma, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Ehsan Abadi, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Anuj Kapadia, 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. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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