Numerical Modeling and High-Speed Parallel Computing: New Perspectives on Tomographic Microwave Imaging for Brain Stroke Detection and Monitoring
Résumé
This article 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 based) with successive solutions of a direct problem such as the accurate modeling of a whole-microwave measurement system. Moreover, a sufficiently high number of unknowns is required to accurately represent the solution. As the system will be used for detecting a brain stroke (ischemic or hemorrhagic) as well as for monitoring during the treatment, the running times for the reconstructions should be reasonable. The method used is based on high-order finite elements, parallel preconditioners from the domain decomposition method and domain-specific language with the opensource FreeFEM++ solver.
Mots clés
Boundary conditions
Brain modeling
inverse problem
Computational modeling
Finite element analysis
brain stroke imaging
Tomography
ischemic brain stroke detection
iterative microwave tomographic imaging
high-speed parallel computing
high-order finite elements
hemorrhagic brain stroke detection
domain-specific language
gradient based minimization algorithm
domain decomposition method
parallel programming
medical image processing
Antenna measurements
optical tomography
whole-microwave measurement system
parallel preconditioners
open source FreeFEM++ solver
numerical modeling
massively parallel computing