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Proceedings Paper

Estimation of non-solid lung nodule volume with low-dose CT protocols: effect of reconstruction algorithm and measurement method
Author(s): Marios A. Gavrielides; Gino DeFilippo; Benjamin P. Berman; Qin Li; Nicholas Petrick; Kurt Schultz; Jenifer Siegelman
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Paper Abstract

Computed tomography is primarily the modality of choice to assess stability of nonsolid pulmonary nodules (sometimes referred to as ground-glass opacity) for three or more years, with change in size being the primary factor to monitor. Since volume extracted from CT is being examined as a quantitative biomarker of lung nodule size, it is important to examine factors affecting the performance of volumetric CT for this task. More specifically, the effect of reconstruction algorithms and measurement method in the context of low-dose CT protocols has been an under-examined area of research. In this phantom study we assessed volumetric CT with two different measurement methods (model-based and segmentation-based) for nodules with radiodensities of both nonsolid (-800HU and -630HU) and solid (-10HU) nodules, sizes of 5mm and 10mm, and two different shapes (spherical and spiculated). Imaging protocols included CTDIvol typical of screening (1.7mGy) and sub-screening (0.6mGy) scans and different types of reconstruction algorithms across three scanners. Results showed that radio-density was the factor contributing most to overall error based on ANOVA. The choice of reconstruction algorithm or measurement method did not affect substantially the accuracy of measurements; however, measurement method affected repeatability with repeatability coefficients ranging from around 3-5% for the model-based estimator to around 20-30% across reconstruction algorithms for the segmentation–based method. The findings of the study can be valuable toward developing standardized protocols and performance claims for nonsolid nodules.

Paper Details

Date Published: 9 March 2017
PDF: 8 pages
Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101322P (9 March 2017); doi: 10.1117/12.2255982
Show Author Affiliations
Marios A. Gavrielides, U.S. Food and Drug Administration (United States)
Gino DeFilippo, U.S. Food and Drug Administration (United States)
Univ. of Maryland, College Park (United States)
Benjamin P. Berman, U.S. Food and Drug Administration (United States)
Qin Li, U.S. Food and Drug Administration (United States)
Nicholas Petrick, U.S. Food and Drug Administration (United States)
Kurt Schultz, Toshiba Medical Research Institute USA, Inc. (United States)
Jenifer Siegelman, Brigham and Women's Hospital, Harvard Medical School (United States)

Published in SPIE Proceedings Vol. 10132:
Medical Imaging 2017: Physics of Medical Imaging
Thomas G. Flohr; Joseph Y. Lo; Taly Gilat Schmidt, Editor(s)

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