The effects of iterative reconstruction in CT on low-contrast liver lesion volumetry: a phantom study

Qin Li; Benjamin P. Berman; Justin Schumacher; Yongguang Liang; Marios A. Gavrielides; Hao Yang; Binsheng Zhao; Nicholas Petrick

doi:10.1117/12.2255743

3 March 2017 The effects of iterative reconstruction in CT on low-contrast liver lesion volumetry: a phantom study

Qin Li, Benjamin P. Berman, Justin Schumacher, Yongguang Liang, Marios A. Gavrielides, Hao Yang, Binsheng Zhao, Nicholas Petrick

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101340Z (2017) https://doi.org/10.1117/12.2255743
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

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Qin Li, Benjamin P. Berman, Justin Schumacher, Yongguang Liang, Marios A. Gavrielides, Hao Yang, Binsheng Zhao, and Nicholas Petrick "The effects of iterative reconstruction in CT on low-contrast liver lesion volumetry: a phantom study", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340Z (3 March 2017); https://doi.org/10.1117/12.2255743

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