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Proceedings Paper

A new method for predicting CT lung nodule volume measurement performance
Author(s): Ricardo S. Avila; Artit Jirapatnakul; Raja Subramaniam; David Yankelevitz
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Paper Abstract

Purpose: To evaluate a new approach for predicting nodule volume measurement bias and variability when scanning with a specific CT scanner and acquisition protocol. Methods: A GE LightSpeed VCT scanner was used to scan 3 new rolls of 3M 3/4 x 1000 Inch Scotch Magic tape with a routine chest protocol (120 kVp, 100 mA, 0.4 s rotation, .98 pitch, STANDARD kernel) at three different slice thicknesses and spacings. Each tape scan was independently analyzed by fully automated image quality assessment software, producing fundamental image quality characteristics and simulated lung nodule volume measurements for a range of sphere diameters. The same VCT scanner and protocol was then used to obtain 10 repeat CT scans of an anthropomorphic chest phantom containing multiple Teflon spheres embedded in foam (diameters = 4.76mm, 6.25mm, and 7.94mm). The observed volume of the spheres in the 30 (3 reconstructions per scan) repeat scans was provided by independently developed nodule measurement software. Results: The predicted vs observed mean volume (mm3 ) and CV for 3 slice thicknesses and sphere sizes was obtained. For 0.625mm slice thickness scans the predicted vs observed values were (44.3,0.91)-vs-(48.2,1.17), (110.4,0.51)-vs-(124.1,0.47), and (219.9,0.29)-vs-(250.1,0.34), for 4.76mm, 6.25mm, and 7.94mm spheres respectively. For 1.25mm slice thickness the corresponding values were (42.1,0.98)-vs-(47.6,1.35), (106.9,0.56)-vs-(123.1,0.61), and (214.8,0.32)-vs-(248.8,0.41). For 2.5mm slice thickness the corresponding values were (23.9,9.53)-vs-(36.8,12.50), (77.6,3.84)-vs-(110.5,3.20), and (173.0,1.57)-vs-(233.9,1.32). Conclusion: Volume measurement bias and variability for lung nodules based on nodule size and acquisition protocol can potentially be predicted using a new method that utilizes fundamental image characteristics and simulation.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343Y (3 March 2017); doi: 10.1117/12.2255053
Show Author Affiliations
Ricardo S. Avila, Accumetra, LLC (United States)
Artit Jirapatnakul, Icahn School of Medicine at Mount Sinai (United States)
Raja Subramaniam, Icahn School of Medicine at Mount Sinai (United States)
David Yankelevitz, Icahn School of Medicine at Mount Sinai (United States)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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