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 com-

prehensive integration of information from different image sources and

different 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-diffusion framework. Model personalization relies only on a for-

ward model for the growth process and on image likelihood. We take

advantage of an adaptive sparse grid approximation for efficient infer-

ence via Markov Chain Monte Carlo sampling. The approach can be used

for integrating information from different multi-modal imaging protocols

and can easily be adapted to other tumor growth models.

In  Information Processing in Medical Imaging (IPMI)

Publisher  Springer Verlag

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