TY - GEN

T1 - Automatic tuning of respiratory model for patient-based simulation

AU - Vidal, F. P.

AU - Villard, P.-F.

AU - Lutton, É.

PY - 2013/5/1

Y1 - 2013/5/1

N2 - This paper is an overview of a method recently published in a biomedical journal (IEEE Transactions on Biomedical Engineering, http://tbme.embs.org). 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.

AB - This paper is an overview of a method recently published in a biomedical journal (IEEE Transactions on Biomedical Engineering, http://tbme.embs.org). 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.

KW - Evolutionary computation, inverse problems, medical simulation, adaptive algorithm

M3 - Conference contribution

SP - 225

EP - 231

BT - International Conference on Medical Imaging Using Bio-Inspired and Soft Computing (MIBISOC2013)

CY - Brussels, Belgium

T2 - International Conference on Medical Imaging Using Bio-Inspired and Soft Computing

Y2 - 15 May 2013 through 17 May 2013

ER -