| Medical Image Analysis | Biological Image Analysis | Computational Anatomy | Computational Physiology | Previous Themes |
We propose to model and simulate the growth of glioblastomas, the most aggressive of the glial tumors. The proposed simulation is based on a model coupling the invasion of the glioblastoma and its mechanical interaction with the invaded structures. This model uses a reaction-diffusion equation for the tumor expansion characterization and the usual continuum mechanics laws for the brain parenchyma behavior. In addition, we propose a new equation to couple these two equations to take into account the mechanical influence of the tumor cells on the invaded tissues.
Our model relies upon an anatomical atlas including cerebral structures having a distinct response to the tumor aggression. In addition, we included in this atlas the information from the Diffusion Tensor Images (DTI) to model the tumor preferential growth in the white fibers direction. Finally, the tumor growth model is used to simulate a virtual GBM grow into a patient brain. This in-silico growth is compared to the real GBM growth observed with a second patient MRI taken 6 months later.
More details can be found here
Contact: Olivier Clatz
Some publications can be found here.
In radiotherapy treatment, the constant margin taken around the visible tumor is a very coarse approximation of the invasion margin of cancerous cells. This work tries to solve the problem of adapting the radiotherapy regions to the tumor growth dynamics. The method proposes approximate invasion margins of the tumor based on its growth dynamics. Determining radiotherapy regions based on these invasion margins would increase the effectiveness of the treatment. The low density tumor parts, undetectable by the current imaging techniques, are extrapolated in a magnetic resonance image (MRI). The extrapolation takes into account the underlying tissue structure (grey matter, white matter, fiber directions), the tumor growth dynamics approximated by the Fisher-Kolmogorov model and the segmented tumor in the image.
Contact: Ender Konukoglu and Hervé Delingette
We are working on an electromechanical model of the heart, simple enough to be used in the deformable model framework, in order to extract some ventricular function parameters from cardiac image sequences and realistic enough to provide predictive information.
We compute the action potential electrical wave propagation using the FitzHugh-Nagumo approach and this potential triggers the muscle contraction, through an electromechanical coupling based on the Hill-Maxwell model.
This model interacts with 4D cardiac images through external forces applied on the surface nodes and computed from the image features. We expect that the a priori information provided by the model will help segment sparse and noisy images. Moreover, this model could help simulate some electrical and mechanical pathologies.
Contact: Maxime Sermesant
Some publications can be found here.
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