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Modeling Glioma Growth and Personalizing Growth Models in Medical Images

Ender Konukoglu 1
1 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Mathematical models and more specifically reaction-diffusion based models have been widely used in the literature for modeling the growth of brain gliomas and tumors in general. Besides the vast amount of research focused on microscopic and biological experiments, recently models have started integrating medical images in their formulations. By including the geometry of the brain and the tumor, the different tissue structures and the diffusion images, models are able to simulate the macroscopic growth observable in the images. Although generic models have been proposed, methods for adapting these models to individual patient images remain an unexplored area. In this thesis we address the problem of "personalizing mathematical tumor growth models". We focus on reaction-diffusion models and their applications on modeling the growth of brain gliomas. As a first step, we propose a method for automatic identification of patient-specific model parameters from series of medical images. Observing the discrepancies between the visualization of gliomas in MR images and the reaction-diffusion models, we derive a novel formulation for explaining the evolution of the tumor delineation. This "modified anisotropic Eikonal model" is later used for estimating the model parameters from images. Thorough analysis on synthetic dataset validates the proposed method theoretically and also gives us insights on the nature of the underlying problem. Preliminary results on real cases show promising potentials of the parameter estimation method and the reaction-diffusion models both for quantifying tumor growth and also for predicting future evolution of the pathology. Following the personalization, we focus on the clinical application of such patient-specific models. Specifically, we tackle the problem of limited visualization of glioma infiltration in MR images. The images only show a part of the tumor and mask the low density invasion. This missing information is crucial for radiotherapy and other types of treatment. We propose a formulation for this problem based on the patient-specific models. In the analysis we also show the potential benefits of such the proposed method for radiotherapy planning. The last part of this thesis deals with numerical methods for anisotropic Eikonal equations. This type of equation arises in both of the previous parts of this thesis. Moreover, such equations are also used in different modeling problems, computer vision, geometrical optics and other different fields. We propose a numerical method for solving anisotropic Eikonal equations in a fast and accurate manner. By comparing it with a state-of-the-art method we demonstrate the advantages of our technique.
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Submitted on : Wednesday, October 19, 2011 - 10:55:19 AM
Last modification on : Friday, January 18, 2019 - 1:19:51 AM
Long-term archiving on: : Friday, January 20, 2012 - 10:26:29 AM


  • HAL Id : tel-00633697, version 1



Ender Konukoglu. Modeling Glioma Growth and Personalizing Growth Models in Medical Images. Human-Computer Interaction [cs.HC]. Université Nice Sophia Antipolis, 2009. English. ⟨NNT : 2009NICE4000⟩. ⟨tel-00633697⟩



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