Blood Perfusion Parameter Estimation in Tumors by means of a Genetic Algorithm

Cancer is nowadays one of the leading causes of death in the world, with growth potential in the coming decades. This work is concerned with an inverse analysis by considering the solution of Pennes bioheat equation in an attempt to set the value of blood perfusion to several cases. To this end, a genetic algorithm (GA) was implemented and coupled with a finite element method (FEM) model that reproduces the heat generation phenomenon caused by the tumor. The GA supplies blood perfusion values for the discretized model by FEM and receives from it a temperature profile generated due to those perfusions. The temperature profile is then compared with a reference profile with the objective of minimizing the error with the aid of GA. Three different selection methods were implemented, as well as miscellaneous other GA parameters in order to find the best parameters that allow accurate results. The results show that the model here implemented is perfectly able to represent the phenomenon and provide accurate data for blood perfusion. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science