Microwave tomography for brain stroke imaging

Abstract

This paper deals with microwave tomography for brain stroke imaging using state-of-the-art numerical modeling and massively parallel computing. Iterative microwave tomographic imaging requires the solution of an inverse problem based on a minimization algorithm (e.g. gradient or Newton-like methods) with successive solutions of a direct problem. The solution direct requests an accurate modeling of the whole-microwave measurement system as well as the as the whole-head. Moreover, as the system will be used for detecting brain strokes (ischemic or hemorrhagic) and for monitoring during the treatment, running times for the reconstructions should be fast. The method used is based on high-order finite elements, parallel preconditioners with the Domain Decomposition method and Domain Specific Language with open source FreeFEM++ solver.