Volume estimation of multidensity nodules with thoracic computed tomography

Marios A. Gavrielides; Qin Li; Rongping Zeng; Kyle J. Myers; Berkman Sahiner; Nicholas A. Petrick

doi:10.1117/1.JMI.3.1.013504

29 January 2016 Volume estimation of multidensity nodules with thoracic computed tomography

Marios A. Gavrielides, Qin Li, Rongping Zeng, Kyle J. Myers, Berkman Sahiner, Nicholas A. Petrick

Author Affiliations +

Journal of Medical Imaging, Vol. 3, Issue 1, 013504 (January 2016). https://doi.org/10.1117/1.JMI.3.1.013504

Abstract

This work focuses on volume estimation of “multidensity” lung nodules in a phantom computed tomography study. Eight objects were manufactured by enclosing spherical cores within larger spheres of double the diameter but with a different density. Different combinations of outer-shell/inner-core diameters and densities were created. The nodules were placed within an anthropomorphic phantom and scanned with various acquisition and reconstruction parameters. The volumes of the entire multidensity object as well as the inner core of the object were estimated using a model-based volume estimator. Results showed percent volume bias across all nodules and imaging protocols with slice thicknesses <5 mm ranging from −5.1% to 6.6% for the entire object (standard deviation ranged from 1.5% to 7.6%), and within −12.6% to 5.7% for the inner-core measurement (standard deviation ranged from 2.0% to 17.7%). Overall, the estimation error was larger for the inner-core measurements, which was expected due to the smaller size of the core. Reconstructed slice thickness was found to substantially affect volumetric error for both tasks; exposure and reconstruction kernel were not. These findings provide information for understanding uncertainty in volumetry of nodules that include multiple densities such as ground glass opacities with a solid component.

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Marios A. Gavrielides, Qin Li, Rongping Zeng, Kyle J. Myers, Berkman Sahiner, and Nicholas A. Petrick "Volume estimation of multidensity nodules with thoracic computed tomography," Journal of Medical Imaging 3(1), 013504 (29 January 2016). https://doi.org/10.1117/1.JMI.3.1.013504

Published: 29 January 2016

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