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1 March 2019 Accuracy and variability of radiomics in photon-counting CT: texture features and lung lesion morphology
Jayasai R. Rajagopal, Jocelyn Hoye, Marthony Robins, Elizabeth C. Jones M.D., Ehsan Samei
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Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109484J (2019) https://doi.org/10.1117/12.2512934
Event: SPIE Medical Imaging, 2019, San Diego, California, United States
Abstract
The purpose of this study was to evaluate the potential of a prototype photon-counting CT system scanner to characterize liver texture and lung lesion morphology features. We utilized a multi-tiered phantom (Mercury Phantom 4.0) to characterize the noise power spectrum and task-transfer functions of both conventional and photoncounting modes on the scanner. Using these metrics, we blurred three textures models and fifteen model lesions for four doses (CTDIvol: 4, 8, 16, 24 mGy), and three slice thicknesses (1.6, 2.5, 4 mm), for a total of 12 imaging conditions. Twenty texture features and twenty-one morphology features were evaluated. Performance was characterized in terms of accuracy (percent bias of features across different conditions) and variability (coefficient of variation of features due to repeats and averaged across conditions). Compared to conventional CT, photon-counting CT had comparable accuracy and variability for texture features. For morphology features, photon-counting CT had comparable accuracy and less variability than conventional CT. For both imaging modes, change in dose showed slight variation in features and increasing slice thickness caused a monotonic change with feature dependent directionality. Photon-counting CT can improve the characterization of morphology features without compromising texture features.
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Jayasai R. Rajagopal, Jocelyn Hoye, Marthony Robins, Elizabeth C. Jones M.D., and Ehsan Samei "Accuracy and variability of radiomics in photon-counting CT: texture features and lung lesion morphology", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109484J (1 March 2019); https://doi.org/10.1117/12.2512934
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KEYWORDS
Computed tomography
Lung
Imaging systems
X-ray computed tomography
Radiology
Photon counting
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