PURPOSE: To develop and validate a technique based on characteristics of real lesions for simulating realistic small liver lesions on pediatric computed tomographic (CT) images. MATERIALS AND METHODS: The institutional review board provided exempt status for this study, determined that it was not subject to HIPAA compliance, and did not require informed consent. Patient identification information was removed from clinical images from contrast material-enhanced multi-detector row CT examinations performed in 10 children. Patients were infants or children up to 18 years old. Information about sex was not available. Children had one or more liver lesions of 2-6 mm in maximum transverse diameter. Images with more than one lesion were rendered multiple times, and each time, all but one of the lesions were digitally removed in sequence. This process provided images (n = 19) with a single real lesion. For consistency, the same image backgrounds (images with all real lesions removed) were used to create an identical number of images (n = 19), each with a single simulated lesion. Subsequently, three radiologists independently assessed images of real and simulated lesions that were presented in random order with a score on a continuous scale of 0 (definitely simulated) to 100 (definitely real). Mixed-model analysis of variance was used to test the null hypothesis that the difference in population mean scores between the two lesion types was zero. RESULTS: The observer study did not reveal a significant difference in the ability of any radiologist to discriminate between real and simulated lesions (P > .31). The differences in mean scores for discrimination between real and simulated lesions for the three observers were -6, 9, and -7, respectively. The estimated overall difference was -1. CONCLUSION: Mathematic simulation of liver lesions is a feasible technique for creating realistic lesions for image quality or dose reduction studies in pediatric CT. (c) RSNA, 2005
PURPOSE: To develop and validate a technique based on characteristics of real lesions for simulating realistic small liver lesions on pediatric computed tomographic (CT) images. MATERIALS AND METHODS: The institutional review board provided exempt status for this study, determined that it was not subject to HIPAA compliance, and did not require informed consent. Patient identification information was removed from clinical images from contrast material-enhanced multi-detector row CT examinations performed in 10 children. Patients were infants or children up to 18 years old. Information about sex was not available. Children had one or more liver lesions of 2-6 mm in maximum transverse diameter. Images with more than one lesion were rendered multiple times, and each time, all but one of the lesions were digitally removed in sequence. This process provided images (n = 19) with a single real lesion. For consistency, the same image backgrounds (images with all real lesions removed) were used to create an identical number of images (n = 19), each with a single simulated lesion. Subsequently, three radiologists independently assessed images of real and simulated lesions that were presented in random order with a score on a continuous scale of 0 (definitely simulated) to 100 (definitely real). Mixed-model analysis of variance was used to test the null hypothesis that the difference in population mean scores between the two lesion types was zero. RESULTS: The observer study did not reveal a significant difference in the ability of any radiologist to discriminate between real and simulated lesions (P > .31). The differences in mean scores for discrimination between real and simulated lesions for the three observers were -6, 9, and -7, respectively. The estimated overall difference was -1. CONCLUSION: Mathematic simulation of liver lesions is a feasible technique for creating realistic lesions for image quality or dose reduction studies in pediatric CT. (c) RSNA, 2005
Authors: Baiyu Chen; Chi Ma; Shuai Leng; Jeff L Fidler; Shannon P Sheedy; Cynthia H McCollough; Joel G Fletcher; Lifeng Yu Journal: Acad Radiol Date: 2016-07-16 Impact factor: 3.173
Authors: Chi Ma; Lifeng Yu; Baiyu Chen; Chi Wan Koo; Edwin A Takahashi; Joel G Fletcher; David L Levin; Ronald S Kuzo; Lyndsay D Viers; Stephanie A Vincent-Sheldon; Shuai Leng; Cynthia H McCollough Journal: J Med Imaging (Bellingham) Date: 2017-03-31
Authors: Chi Ma; Baiyu Chen; Chi Wan Koo; Edwin A Takahashi; Joel G Fletcher; Cynthia H McCollough; David L Levin; Ronald S Kuzo; Lyndsay D Viers; Stephanie A Vincent Sheldon; Shuai Leng; Lifeng Yu Journal: Proc SPIE Int Soc Opt Eng Date: 2016-04-04
Authors: Ehsan Abadi; William P Segars; Benjamin M W Tsui; Paul E Kinahan; Nick Bottenus; Alejandro F Frangi; Andrew Maidment; Joseph Lo; Ehsan Samei Journal: J Med Imaging (Bellingham) Date: 2020-04-11
Authors: Christian von Falck; Vesela Bratanova; Thomas Rodt; Bernhard Meyer; Stephan Waldeck; Frank Wacker; Hoen-oh Shin Journal: PLoS One Date: 2013-02-13 Impact factor: 3.240