Author:
Hong Dayeong,Moon Sojin,Seo Joon Beom,Kim Namkug
Abstract
AbstractThe validation of the accuracy of the quantification software in computed tomography (CT) images is very challenging. Therefore, we proposed a CT imaging phantom that accurately represents patient-specific anatomical structures and randomly integrates various lesions including disease-like patterns and lesions of various shapes and sizes using silicone casting and three-dimensional (3D) printing. Six nodules of various shapes and sizes were randomly added to the patient’s modeled lungs to evaluate the accuracy of the quantification software. By using silicone materials, CT intensities suitable for the lesions and lung parenchyma were realized, and their Hounsfield unit (HU) values were evaluated on a CT scan of the phantom. As a result, based on the CT scan of the imaging phantom model, the measured HU values for the normal lung parenchyma, each nodule, fibrosis, and emphysematous lesions were within the target value. The measurement error between the stereolithography model and 3D-printing phantoms was 0.2 ± 0.18 mm. In conclusion, the use of 3D printing and silicone casting allowed the application and evaluation of the proposed CT imaging phantom for the validation of the accuracy of the quantification software in CT images, which could be applied to CT-based quantification and development of imaging biomarkers.
Funder
Korea Health Industry Development Institute
Publisher
Springer Science and Business Media LLC
Reference35 articles.
1. Zhao, J., Zhang, Y., He, X. & Xie, P. Covid-ct-dataset: A ct scan dataset about covid-19. arXiv preprint arXiv:2003.13865490 (2020).
2. Shah, V. et al. Diagnosis of COVID-19 using CT scan images and deep learning techniques. Emerg. Radiol. 28, 497–505 (2021).
3. Tenda, E. D. et al. The importance of chest CT scan in COVID-19: A case series. Acta Med. Indones 52, 68–73 (2020).
4. Hong, D. et al. Development of a personalized and realistic educational thyroid cancer phantom based on CT images: An evaluation of accuracy between three different 3D printers. Comput. Biol. Med. 113, 103393 (2019).
5. Hong, D., Kim, H., Kim, T., Kim, Y.-H. & Kim, N. Development of patient specific, realistic, and reusable video assisted thoracoscopic surgery simulator using 3D printing and pediatric computed tomography images. Sci. Rep. 11, 1–10 (2021).
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献