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

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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https://hal.archives-ouvertes.fr/hal-01623106
Contributor : Sophie Gaffé-Clément <>
Submitted on : Wednesday, October 25, 2017 - 9:17:11 AM
Last modification on : Saturday, April 11, 2020 - 1:59:37 AM

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Pierre-Henri Tournier, Marcella Bonazzoli, Victorita Dolean, Francesca Rapetti, Frédéric Hecht, et al.. Numerical Modeling and High-Speed Parallel Computing: New Perspectives on Tomographic Microwave Imaging for Brain Stroke Detection and Monitoring.. IEEE Antennas and Propagation Magazine, Institute of Electrical and Electronics Engineers, 2017, 59 (5), pp.98 - 110. ⟨10.1109/MAP.2017.2731199⟩. ⟨hal-01623106⟩

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