Quantitative CT: technique dependency of volume assessment for pulmonary nodules

Baiyu Chen; Samuel Richard; Huiman Barnhart; James Colsher; Maxwell Amurao; Ehsan Samei

doi:10.1117/12.845493

22 March 2010 Quantitative CT: technique dependency of volume assessment for pulmonary nodules

Baiyu Chen, Samuel Richard, Huiman Barnhart, James Colsher, Maxwell Amurao, Ehsan Samei

Proceedings Volume 7622, Medical Imaging 2010: Physics of Medical Imaging; 76222W (2010) https://doi.org/10.1117/12.845493
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

Current lung nodule size assessment methods typically rely on one-dimensional estimation of lesions. While new 3D volume assessment techniques using MSCT scan data have enabled improved estimation of lesion size, the effect of acquisition and reconstruction parameters on accuracy and precision of such estimation has not been adequately investigated. To characterize such dependencies, we scanned an anthropomorphic thoracic phantom containing synthetic nodules with different protocols, including various acquisition and reconstruction parameters. We also scanned the phantom repeatedly with the same protocol to investigate repeatability. The nodule's volume was estimated by a clinical lung analysis software package, LungVCAR. Accuracy (bias) and precision (variance) of the volume assessment were calculated across the nodules and compared between protocols via Generalized Estimating Equation analysis. Results suggest a strong dependence of accuracy and precision on dose level but little dependence on reconstruction thickness, thus providing possible guidelines for protocol optimization for quantitative tasks.

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Baiyu Chen, Samuel Richard, Huiman Barnhart, James Colsher, Maxwell Amurao, and Ehsan Samei "Quantitative CT: technique dependency of volume assessment for pulmonary nodules", Proc. SPIE 7622, Medical Imaging 2010: Physics of Medical Imaging, 76222W (22 March 2010); https://doi.org/10.1117/12.845493

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KEYWORDS

3D imaging standards

Computed tomography

Image segmentation

Lung

Statistical analysis

3D image reconstruction

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