Quantitative CT: technique dependence of volume estimation on pulmonary nodules

Physics in Medicine and Biology
Baiyu ChenEhsan Samei

Abstract

Current estimation of lung nodule size typically relies on uni- or bi-dimensional techniques. While new three-dimensional volume estimation techniques using MDCT have improved size estimation of nodules with irregular shapes, the effect of acquisition and reconstruction parameters on accuracy (bias) and precision (variance) of the new techniques has not been fully investigated. To characterize the volume estimation performance dependence on these parameters, an anthropomorphic chest phantom containing synthetic nodules was scanned and reconstructed with protocols across various acquisition and reconstruction parameters. Nodule volumes were estimated by a clinical lung analysis software package, LungVCAR. Precision and accuracy of the volume assessment were calculated across the nodules and compared between protocols via a generalized estimating equation analysis. Results showed that the precision and accuracy of nodule volume quantifications were dependent on slice thickness, with different dependences for different nodule characteristics. Other parameters including kVp, pitch, and reconstruction kernel had lower impact. Determining these technique dependences enables better volume quantification via protocol optimization and h...Continue Reading

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Citations

Feb 22, 2014·AJR. American Journal of Roentgenology·Xueqian XieMarcel J W Greuter
Dec 8, 2014·Academic Radiology·Agostino GibaldiAlessandro Bevilacqua
Oct 15, 2013·Academic Radiology·Marios A GavrielidesNicholas Petrick
Jun 13, 2014·Statistical Methods in Medical Research·Nancy A ObuchowskiUNKNOWN Case Example Working Group
Apr 7, 2017·Journal of Neural Engineering·I Delgado-MartínezX Navarro
Apr 24, 2020·Korean Journal of Radiology : Official Journal of the Korean Radiological Society·Eui Jin Hwang, Chang Min Park
Oct 18, 2014·Physics in Medicine and Biology·Justin Solomon, Ehsan Samei
Jul 25, 2019·Journal of Medical Imaging·Marthony RobinsEhsan Samei

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