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Bias and variability in morphology features of lung lesions across CT imaging conditions
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Paper Abstract

CT imaging method can influence radiomic features. The purpose of this study was to characterize the intra-protocol and inter-protocol variability and bias of quantitative morphology features of lung lesions across a range of CT imaging conditions. A total of 15 lung lesions were simulated (five in each of three spiculation classes: low, medium, and high). For each lesion, a series of simulated CT images representing different imaging conditions were synthesized by applying 3D blur and adding correlated noise based on the measured noise and resolution properties of five commercial multi-slice CT systems, representing three dose levels (CTDIvol of 1.90, 3.75, 7.50 mGy), three slice thicknesses (0.625, 1.25, 2.5 mm), and 33 clinical reconstruction kernels. Five repeated image volumes were synthesized for each lesion and imaging condition. A series of 21 shape-based morphology features were extracted from both “ground truth” (i.e., pre-blur without noise) and “image rendered” lesions (i.e., post-blur and with noise). For each morphology feature, the intra-protocol and inter-protocol variability was characterized by calculating the average coefficient of variation (COV) across repeats and imaging conditions, respectively (average was across all lesions). The bias was quantified by comparing the percent relative error in the morphology metric between the imaged lesions and ground truth lesions. The average intra-protocol COV metric ranged from 0.2% to 3%. The average inter-protocol COV ranged from 3% to 106% with most features being around 30%. Percent relative error was most biased at 73% for Ellipsoid Volume and least biased at -0.27% for Flatness. Results of the study indicate that different reconstructions can lead to significant bias and variability in the measurements of morphological features.

Paper Details

Date Published: 9 March 2018
PDF: 11 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105731Z (9 March 2018); doi: 10.1117/12.2293545
Show Author Affiliations
Jocelyn Hoye, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. Medical Ctr. (United States)
Justin B. Solomon, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. Medical Ctr. (United States)
Thomas Sauer, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. Medical Ctr. (United States)
Marthony Robins, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. Medical Ctr. (United States)
Ehsan Samei, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Duke Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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