title: A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging.
abstract: Realistic three-dimensional (3D) mathematical models of subtle lesions are essential for many computed tomography (CT) studies focused on performance evaluation and optimization. In this paper, we develop a generic mathematical framework that describes the 3D size, shape, contrast, and contrast-profile characteristics of a lesion, as well as a method to create lesion models based on CT data of real lesions. Further, we implemented a technique to insert the lesion models into CT images in order to create hybrid CT datasets. This framework was used to create a library of realistic lesion models and corresponding hybrid CT images. The goodness of fit of the models was assessed using the coefficient of determination (R(2)) and the visual appearance of the hybrid images was assessed with an observer study using images of both real and simulated lesions and receiver operator characteristic (ROC) analysis. The average R(2) of the lesion models was 0.80, implying that the models provide a good fit to real lesion data. The area under the ROC curve was 0.55, implying that the observers could not readily distinguish between real and simulated lesions. Therefore, we conclude that the lesion-modeling framework presented in this paper can be used to create realistic lesion models and hybrid CT images. These models could be instrumental in performance evaluation and optimization of novel CT systems.
authors.affiliation: Duke University Health System, Department of Radiology, 2424 Erwin Rd. Suite 302, Durham, NC 27705, USA.
authors.forename: Justin
authors.initials: J
authors.lastname: Solomon
authors.forename: Ehsan
authors.initials: E
authors.lastname: Samei
mesh_terms: D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007669:Kidney Calculi / Q000000981:diagnostic imaging / Q000473:pathology*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D055613:Multiple Pulmonary Nodules / Q000000981:diagnostic imaging / Q000473:pathology*; D015588:Observer Variation; D012372:ROC Curve; D014057:Tomography, X-Ray Computed / Q000379:methods*