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Proceedings Paper

Detectable change of lung nodule volume with CT in a phantom study with high and low signal to background contrast
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Paper Abstract

In previous work we developed a method for predicting the minimum detectable change (MDC) in nodule volume based on volumetric CT measurements. MDC was defined as the minimum increase/decrease in a nodule volume distinguishable from the baseline measurement at a specified level of detection performance, assessed using the area under the ROC curve (AUC). In this work we derived volume estimates of a set of synthetic nodules and calculated the detection performance for distinguishing them from baseline measurements. Eight spherical objects of 100HU radio density ranging in diameter from 5.0mm to 5.75mm and 8.0mm to 8.75mm with 0.25mm increments were placed in an anthropomorphic phantom with either no background (high-contrast task) or gelatin background (low-contrast task). The baseline was defined as 5.0mm for the first set of nodules and 8.0mm for the second set. The phantom was scanned using varying exposures, and reconstructed with slice thickness of 0.75, 1.5, and 3.0mm and two reconstruction kernels (standard and smooth). Volume measurements were derived using a previously developed matched- filter approach. Results showed that nodule size, slice thickness, and nodule-to-background contrast affected detectable change in nodule volume when using our volume estimator and the acquisition settings from our study. We also compared our experimental results to the values estimated by our previously-developed MDC prediction method. We found that experimental data for the 8mm baseline nodules matched very well with our predicted values of MDC. These results support considering the use of this metric when standardizing imaging protocols for lung nodule size change assessment.

Paper Details

Date Published: 25 March 2016
PDF: 8 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 978329 (25 March 2016); doi: 10.1117/12.2217887
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)
Qi Gong, U.S. Food and Drug Administration (United States)
Kyle 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. 9783:
Medical Imaging 2016: Physics of Medical Imaging
Despina Kontos; Thomas G. Flohr, Editor(s)

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