A Generative Approach for Image-Based Modeling of Tumor Growth

Abstract : Extensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from di fferent image sources and diff erent time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-di ffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from di fferent multi-modal imaging protocols and can easily be adapted to other tumor growth models.
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Székely, Gábor and Hahn, Horst K. Information Processing in Medical Imaging, 22, Springer, Heidelberg/Germany, pp.735-747, 2011, LNCS
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Bjoern H. Menze, Koen Van Leemput, Ender Konukoglu, Marc-André Weber, Nicholas Ayache, et al.. A Generative Approach for Image-Based Modeling of Tumor Growth. Székely, Gábor and Hahn, Horst K. Information Processing in Medical Imaging, 22, Springer, Heidelberg/Germany, pp.735-747, 2011, LNCS. 〈hal-00813801〉

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