Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations

Abstract : Reaction-diffusion based tumor growth models have been widely used in the literature for modeling the growth of brain gliomas. Lately, recent models have started integrating medical images in their formulation. Including different tissue types, geometry of the brain and the directions of white matter fiber tracts improved the spatial accuracy of reaction-diffusion models. The adaptation of the general model to the specific patient cases on the other hand has not been studied thoroughly yet. In this work we address this adaptation. We propose a parameter estimation method for reaction-diffusion tumor growth models using time series of medical images. This method estimates the patient specific parameters of the model using the images of the patient taken at successive time instances. The proposed method formulates the evolution of the tumor delineation visible in the images based on the reaction-diffusion dynamics therefore it remains consistent with the information available. We perform thorough analysis of the method using synthetic tumors and show important couplings between parameters of the reaction-diffusion model. We show that several parameters can be uniquely identified in the case of fixing one parameter, namely the proliferation rate of tumor cells. Moreover, regardless of the value the proliferation rate is fixed to, the speed of growth of the tumor can be estimated in terms of the model parameters with accuracy. We also show that using the model-based speed we can simulate the evolution of the tumor for the specific patient case. Finally we apply our method to 2 real cases and show promising preliminary results.
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Soumis le : lundi 8 juillet 2013 - 16:27:07
Dernière modification le : vendredi 12 janvier 2018 - 01:55:42
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Ender Konukoglu, Olivier Clatz, Bjoern H. Menze, Marc-André Weber, Bram Stieltjes, et al.. Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2010, 29 (1), pp.77-95. 〈10.1109/TMI.2009.2026413〉. 〈inria-00616100〉

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