PURPOSE: To compare algorithms performing material decomposition and classification in dual-energy CT, it is desirable to know the ground truth of the lesion to be analyzed in real patient data. In this work, we developed and validated a framework to insert digital lesions of arbitrary chemical composition into patient projection data acquired on a dual-source, dual-energy CT system. METHODS: A model that takes into account beam-hardening effects was developed to predict the CT number of objects with known chemical composition. The model utilizes information about the x-ray energy spectra, the patient/phantom attenuation, and the x-ray detector energy response. The beam-hardening model was validated on samples of iodine (I) and calcium (Ca) for a second-generation dual-source, dual-energy CT scanner for all tube potentials available and a wide range of patient sizes. The seven most prevalent mineral components of renal stones were modeled and digital stones were created with CT numbers computed for each patient/phantom size and x-ray energy spectra using the developed beam-hardening model. Each digital stone was inserted in the dual-energy projection data of a water phantom scanned on a dual-source scanner and reconstructed with the routine algorithms in use in our practice. The geometry of the forward projection for dual-energy data was validated by comparing CT number accuracy and high-contrast resolution of simulated dual-energy CT data of the ACR phantom with experimentally acquired data. RESULTS: The beam-hardening model and forward projection method accurately predicted the CT number of I and Ca over a wide range of tube potentials and phantom sizes. The images reconstructed after the insertion of digital kidney stones were consistent with the images reconstructed from the scanner, and the CT number ratios for different kidney stone types were consistent with data in the literature. A sample application of the proposed tool was also demonstrated. CONCLUSION: A framework was developed and validated for the creation of digital objects of known mineral composition, and for inserting the digital objects into projection data from a commercial dual-source, dual-energy CT scanner. Among other applications, it will allow a systematic investigation of the impact of scan and reconstruction parameters on kidney stone dual-energy properties under rigorously controlled conditions.
PURPOSE: To compare algorithms performing material decomposition and classification in dual-energy CT, it is desirable to know the ground truth of the lesion to be analyzed in real patient data. In this work, we developed and validated a framework to insert digital lesions of arbitrary chemical composition into patient projection data acquired on a dual-source, dual-energy CT system. METHODS: A model that takes into account beam-hardening effects was developed to predict the CT number of objects with known chemical composition. The model utilizes information about the x-ray energy spectra, the patient/phantom attenuation, and the x-ray detector energy response. The beam-hardening model was validated on samples of iodine (I) and calcium (Ca) for a second-generation dual-source, dual-energy CT scanner for all tube potentials available and a wide range of patient sizes. The seven most prevalent mineral components of renal stones were modeled and digital stones were created with CT numbers computed for each patient/phantom size and x-ray energy spectra using the developed beam-hardening model. Each digital stone was inserted in the dual-energy projection data of a water phantom scanned on a dual-source scanner and reconstructed with the routine algorithms in use in our practice. The geometry of the forward projection for dual-energy data was validated by comparing CT number accuracy and high-contrast resolution of simulated dual-energy CT data of the ACR phantom with experimentally acquired data. RESULTS: The beam-hardening model and forward projection method accurately predicted the CT number of I and Ca over a wide range of tube potentials and phantom sizes. The images reconstructed after the insertion of digital kidney stones were consistent with the images reconstructed from the scanner, and the CT number ratios for different kidney stone types were consistent with data in the literature. A sample application of the proposed tool was also demonstrated. CONCLUSION: A framework was developed and validated for the creation of digital objects of known mineral composition, and for inserting the digital objects into projection data from a commercial dual-source, dual-energy CT scanner. Among other applications, it will allow a systematic investigation of the impact of scan and reconstruction parameters on kidney stone dual-energy properties under rigorously controlled conditions.
Authors: Mingliang Qu; Juan C Ramirez-Giraldo; Shuai Leng; James C Williams; Terri J Vrtiska; John C Lieske; Cynthia H McCollough Journal: AJR Am J Roentgenol Date: 2011-06 Impact factor: 3.959
Authors: Andrew N Primak; Joel G Fletcher; Terri J Vrtiska; Oleksandr P Dzyubak; John C Lieske; Molly E Jackson; James C Williams; Cynthia H McCollough Journal: Acad Radiol Date: 2007-12 Impact factor: 3.173
Authors: Cynthia McCollough; Donovan M Bakalyar; Maryam Bostani; Samuel Brady; Kristen Boedeker; John M Boone; H Heather Chen-Mayer; Olav I Christianson; Shuai Leng; Baojun Li; Michael F McNitt-Gray; Roy A Nilsen; Mark P Supanich; Jia Wang Journal: AAPM Rep Date: 2014-09
Authors: Paul Stolzmann; Marko Kozomara; Natalie Chuck; Michael Müntener; Sebastian Leschka; Hans Scheffel; Hatem Alkadhi Journal: Abdom Imaging Date: 2009-09-02
Authors: Xinhui Duan; Zhoubo Li; Lifeng Yu; Shuai Leng; Ahmed F Halaweish; Joel G Fletcher; Cynthia H McCollough Journal: AJR Am J Roentgenol Date: 2015-12 Impact factor: 3.959