Numerical Modeling and High-Speed Parallel Computing: New Perspectives on Tomographic Microwave Imaging for Brain Stroke Detection and Monitoring

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 successi...

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Bibliographic Details
Published in:IEEE antennas & propagation magazine Vol. 59; no. 5; pp. 98 - 110
Main Authors: Tournier, Pierre-Henri, Bonazzoli, Marcella, Dolean, Victorita, Rapetti, Francesca, Hecht, Frederic, Nataf, Frederic, Aliferis, Iannis, El Kanfoud, Ibtissam, Migliaccio, Claire, de Buhan, Maya, Darbas, Marion, Semenov, Serguei, Pichot, Christian
Format: Magazine Article
Language:English
Published: New York IEEE 01.10.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
Antenna measurements
Boundary conditions
Brain
Brain modeling
Computation
Computational modeling
Domain decomposition methods
Domain specific languages
Electromagnetism
Engineering Sciences
Finite element analysis
Imaging
Inverse problems
Iterative methods
Mathematical models
Medical imaging
Monitoring
Tomography
Antenna measurements
domain decomposition method
inverse problem
Computational modeling
Boundary conditions
parallel preconditioners
optical tomography
whole-microwave measurement system
brain stroke imaging
parallel programming
massively parallel computing
open source FreeFEM++ solver
high-order finite elements
domain-specific language
hemorrhagic brain stroke detection
ischemic brain stroke detection
Tomography
Brain modeling
iterative microwave tomographic imaging
Finite element analysis
gradient based minimization algorithm
medical image processing
high-speed parallel computing
numerical modeling
ISSN:1045-9243, 1558-4143
Online Access:Get full text
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Summary: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.
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ISSN:1045-9243
1558-4143
DOI:10.1109/MAP.2017.2731199