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Proceedings Paper

A template-based approach for the analysis of lung nodules in a volumetric CT phantom study
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Paper Abstract

Volumetric CT has the potential to improve the quantitative analysis of lung nodule size change compared to currently used one-dimensional measurement practices. Towards that goal, we have been conducting studies using an anthropomorphic phantom to quantify sources of volume measurement error. One source of error is the measurement technique or software tool used to estimate lesion volume. In this manuscript, we present a template-based approach which utilizes the properties of the acquisition and reconstruction system to quantify nodule volume. This approach may reduce the error associated with the volume estimation technique, thereby improving our ability to estimate the error directly associated with CT parameters and nodule characteristics. Our estimation approach consists of: (a) the simulation of the object-to-image transformation of a helical CT system, (b) the creation of a bank of simulated 3D nodule templates of varying sizes, and (c) the 3D matching of synthetic nodules - that were attached to lung vasculature and scanned with a 16-slice MDCT system - to the bank of simulated templates to estimate nodule volume. Results based on 10 repeat scans for different protocols and a root mean square error (RMSE) similarity metric showed a relative bias of 88%, 14%, and 4% for the measurement of 5 mm, 8 mm and 10 mm low density nodules (-630 HU) compared to -3%, -6%, and 8% for nodules of +100HU density. However, the relative bias for the small, low density nodules (5 mm, -630 HU), was significantly reduced to 7% when a penalized RMSE metric was used to enforce a symmetry constraint that reduced the impact of attached vessels. The results are promising for the use of this measurement approach as a low-bias estimator of nodule volume which will allow the systematic quantification and ranking of measurement error in volumetric CT analysis of lung nodules.

Paper Details

Date Published: 27 February 2009
PDF: 11 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726009 (27 February 2009); doi: 10.1117/12.813560
Show Author Affiliations
Marios A. Gavrielides, U.S. Food and Drug Administration (United States)
Rongping Zeng, U.S. Food and Drug Administration (United States)
Lisa M. Kinnard, U.S. Food and Drug Administration (United States)
Kyle J. Myers, U.S. Food and Drug Administration (United States)
Nicholas Petrick, U.S. Food and Drug Administration (United States)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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