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Proceedings Paper

The effects of iterative reconstruction in CT on low-contrast liver lesion volumetry: a phantom study
Author(s): Qin Li; Benjamin P. Berman; Justin Schumacher; Yongguang Liang; Marios A. Gavrielides; Hao Yang; Binsheng Zhao; Nicholas Petrick
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Paper Abstract

Tumor volume measured from computed tomography images is considered a biomarker for disease progression or treatment response. The estimation of the tumor volume depends on the imaging system parameters selected, as well as lesion characteristics. In this study, we examined how different image reconstruction methods affect the measurement of lesions in an anthropomorphic liver phantom with a non-uniform background. Iterative statistics-based and model-based reconstructions, as well as filtered back-projection, were evaluated and compared in this study. Statistics-based and filtered back-projection yielded similar estimation performance, while model-based yielded higher precision but lower accuracy in the case of small lesions. Iterative reconstructions exhibited higher signal-to-noise ratio but slightly lower contrast of the lesion relative to the background. A better understanding of lesion volumetry performance as a function of acquisition parameters and lesion characteristics can lead to its incorporation as a routine sizing tool.

Paper Details

Date Published: 3 March 2017
PDF: 15 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340Z (3 March 2017); doi: 10.1117/12.2255743
Show Author Affiliations
Qin Li, U.S. Food and Drug Administration (United States)
Benjamin P. Berman, U.S. Food and Drug Administration (United States)
Justin Schumacher, Univ. of Rochester (United States)
Yongguang Liang, Columbia Univ. Medical Ctr. (United States)
Marios A. Gavrielides, U.S. Food and Drug Administration (United States)
Hao Yang, Columbia Univ. Medical Ctr. (United States)
Binsheng Zhao, Columbia Univ. Medical Ctr. (United States)
Nicholas Petrick, U.S. Food and Drug Administration (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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