Microwave tomography for brain stroke imaging
Résumé
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.
Mots clés
Microwave Imaging
iterative methods
medical image processing
computerised tomography
massively parallel computing
Newton method
biomedical imaging
brain
inverse problem
direct problem
iterative microwave
brain stroke imaging
image reconstruction
finite element analysis
Antenna measurements
Microwave theory and techniques
Permittivity
Tomography
Brain Strokes
Parallel computing
Microwave Tomography
Newton-like methods
Inverse Problems
microwave measurement
numerical modeling
whole-microwave measurement system