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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| Vydáno v: | IEEE antennas & propagation magazine Ročník 59; číslo 5; s. 98 - 110 |
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| Hlavní autoři: | , , , , , , , , , , , , |
| Médium: | Magazine Article |
| Jazyk: | angličtina |
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New York
IEEE
01.10.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
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| ISSN: | 1045-9243, 1558-4143 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Semenov, Serguei Dolean, Victorita Hecht, Frederic Darbas, Marion Pichot, Christian de Buhan, Maya Nataf, Frederic Bonazzoli, Marcella El Kanfoud, Ibtissam Aliferis, Iannis Migliaccio, Claire Tournier, Pierre-Henri Rapetti, Francesca |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | Numerical Modeling and High-Speed Parallel Computing: New Perspectives on Tomographic Microwave Imaging for Brain Stroke Detection and Monitoring |
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