A Method to Compute Respiration Parameters for Patient-based Simulators

Best Poster AwardInternational audienceWe propose a method to automatically tune a patient-based virtual environment training simulator for abdominal needle insertion. The key attributes to be customized in our framework are the elasticity of soft-tissues and the respiratory model parameters. The estimation is based on two 3D Computed Tomography (CT) scans of the same patient at two different time steps. Results are presented on four patients and show that our new method leads to better results than our previous studies with manually tuned parameters

Automatic tuning of respiratory model for patient-based simulation

International audienceThis paper is an overview of a method recently pub- lished in a biomedical journal (IEEE Transactions on Biomedical Engineering1). The method is based on an optimisation technique called "evolutionary strategy" and it has been designed to estimate the parameters of a complex 15-D respiration model. This model is adaptable to account for patient's specificities. The aim of the optimisation algorithm is to finely tune the model so that it accurately fits real patient datasets. The final results can then be embedded, for example, in high fidelity simulations of the human physiology. Our algorithm is fully automatic and adaptive. A compound fitness function has been designed to take into account for various quantities that have to be minimised (here topological errors of the liver and the diaphragm geometries). The performance our implementation is compared with two traditional methods (downhill simplex and conjugate gradient descent), a random search and a basic real- valued genetic algorithm. It shows that our evolutionary scheme provides results that are significantly more stable and accurate than the other tested methods. The approach is relatively generic and can be easily adapted to other complex parametrisation problems when ground truth data is available