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Proceedings Paper

Volume estimation of multi-density nodules with thoracic CT
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Paper Abstract

The purpose of this work was to quantify the effect of surrounding density on the volumetric assessment of lung nodules in a phantom CT study. Eight synthetic multidensity nodules were manufactured by enclosing spherical cores in larger spheres of double the diameter and with a different uniform density. Different combinations of outer/inner diameters (20/10mm, 10/5mm) and densities (100HU/-630HU, 10HU/- 630HU, -630HU/100HU, -630HU/-10HU) were created. The nodules were placed within an anthropomorphic phantom and scanned with a 16-detector row CT scanner. Ten repeat scans were acquired using exposures of 20, 100, and 200mAs, slice collimations of 16x0.75mm and 16x1.5mm, and pitch of 1.2, and were reconstructed with varying slice thicknesses (three for each collimation) using two reconstruction filters (medium and standard). The volumes of the inner nodule cores were estimated from the reconstructed CT data using a matched-filter approach with templates modeling the characteristics of the multi-density objects. Volume estimation of the inner nodule was assessed using percent bias (PB) and the standard deviation of percent error (SPE). The true volumes of the inner nodules were measured using micro CT imaging. Results show PB values ranging from -12.4 to 2.3% and SPE values ranging from 1.8 to 12.8%. This study indicates that the volume of multi-density nodules can be measured with relatively small percent bias (on the order of ±12% or less) when accounting for the properties of surrounding densities. These findings can provide valuable information for understanding bias and variability in clinical measurements of nodules that also include local biological changes such as inflammation and necrosis.

Paper Details

Date Published: 19 March 2014
PDF: 10 pages
Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 903331 (19 March 2014); doi: 10.1117/12.2043833
Show Author Affiliations
Marios A. Gavrielides, U.S. Food and Drug Administration (United States)
Qin Li, U.S. Food and Drug Administration (United States)
Rongping Zeng, U.S. Food and Drug Administration (United States)
Kyle J. Myers, U.S. Food and Drug Administration (United States)
Berkman Sahiner, U.S. Food and Drug Administration (United States)
Nicholas Petrick, U.S. Food and Drug Administration (United States)


Published in SPIE Proceedings Vol. 9033:
Medical Imaging 2014: Physics of Medical Imaging
Bruce R. Whiting; Christoph Hoeschen, Editor(s)

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