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4 April 2022 Optimization of imaging parameters of an investigational photon-counting CT prototype for lung lesion radiomics
Cindy McCabe, Mojtaba Zarei, W. Paul Segars, Ehsan Samei, Ehsan Abadi
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Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203337 (2022) https://doi.org/10.1117/12.2612973
Event: SPIE Medical Imaging, 2022, San Diego, California, United States
Abstract
The aim of this study was to evaluate and optimize the imaging parameters of a new dual-source photon-counting CT (PCCT) scanner (NAEOTOM Alpha, Siemens Healthineers) for lung lesion radiomics using virtual imaging trials. Virtual patients (XCAT phantoms) were modeled at three BMIs (22%, 52%, and 88%), with three lesions in each phantom. The lesions were modeled with varying spiculation levels (low, medium, high). A scanner-specific CT simulator (DukeSim), setup to model the NAEOTOM Alpha scanner, was used to simulate imaging of the virtual patients under varying radiation dose (5.7 to 17.1 mGy) and reconstruction parameters (matrix size of 512x512 and 1024x1024, kernels of Bl56, Br56, and Qr56, and slice thicknesses of 0.4 to 3.0 mm). A morphological snakes segmentation method was used to segment the lesions in the reconstructed images. The segmented masks were used to calculate morphological radiomic features across all acquired images. The original phantoms were also run through the same radiomics software to serve as ground truth measurements. The radiomics features were found to be most dependent on slice thickness and least dependent on dose level. By increasing the dose from 5.7 mGy to 17.1 mGy the accuracy in the radiomics measurements increased at most by 2.0%. The Qr56 kernel, 0.34 mm in-plane pixel size and 0.4 mm slice thickness had the more accurate measurements of morphological features (e.g., error of 6.7 ± 5.6 % vs. 11.8 ± 9.6% for mesh volume).
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Cindy McCabe, Mojtaba Zarei, W. Paul Segars, Ehsan Samei, and Ehsan Abadi "Optimization of imaging parameters of an investigational photon-counting CT prototype for lung lesion radiomics", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203337 (4 April 2022); https://doi.org/10.1117/12.2612973
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KEYWORDS
Image segmentation
Lung
Computed tomography
Lung cancer
Cancer
Photon counting
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