16 November 2017 Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon-counting detector computed tomography system
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Abstract
An ultra-high resolution (UHR) mode, with a detector pixel size of 0.25 mm × 0.25 mm relative to isocenter, has been implemented on a whole body research photon-counting detector (PCD) computed tomography (CT) system. Twenty synthetic lung nodules were scanned using UHR and conventional resolution (macro) modes and reconstructed with medium and very sharp kernels. Linear regression was used to compare measured nodule volumes from CT images to reference volumes. The full-width-at-half-maximum of the calculated curvature histogram for each nodule was used as a shape index, and receiver operating characteristic analysis was performed to differentiate sphere- and star-shaped nodules. Results showed a strong linear relationship between measured nodule volumes and reference volumes for both modes. The overall volume estimation was more accurate using UHR mode and the very sharp kernel, having 4.8% error compared with 10.5% to 12.6% error in the macro mode. The improvement in volume measurements using the UHR mode was more evident for small nodule sizes or star-shaped nodules. Images from the UHR mode with the very sharp kernel consistently demonstrated the best performance [ AUC = ( 0.839 , 0.867 ) ] for separating star- from sphere-shaped nodules, showing advantages of UHR mode on a PCD CT scanner for lung nodule characterization.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Wei Zhou, Wei Zhou, Juan Montoya, Juan Montoya, Ralf Gutjahr, Ralf Gutjahr, Andrea Ferrero, Andrea Ferrero, Ahmed Halaweish, Ahmed Halaweish, Steffen Kappler, Steffen Kappler, Cynthia H. McCollough, Cynthia H. McCollough, Shuai Leng, Shuai Leng, } "Lung nodule volume quantification and shape differentiation with an ultra-high resolution technique on a photon-counting detector computed tomography system," Journal of Medical Imaging 4(4), 043502 (16 November 2017). https://doi.org/10.1117/1.JMI.4.4.043502 . Submission: Received: 8 June 2017; Accepted: 1 November 2017
Received: 8 June 2017; Accepted: 1 November 2017; Published: 16 November 2017
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