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

Published

Conference Paper

© SPIE. Downloading of the abstract is permitted for personal use only. 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.

Full Text

Duke Authors

Cited Authors

  • Sharma, S; Abadi, E; Kapadia, A; Segars, WP; Samei, E

Published Date

  • January 1, 2019

Published In

Volume / Issue

  • 10948 /

International Standard Serial Number (ISSN)

  • 1605-7422

International Standard Book Number 13 (ISBN-13)

  • 9781510625433

Digital Object Identifier (DOI)

  • 10.1117/12.2513198

Citation Source

  • Scopus