Numerical Modeling and High-Speed Parallel Computing: New Perspectives on Tomographic Microwave Imaging for Brain Stroke Detection and Monitoring - Université Pierre et Marie Curie Accéder directement au contenu
Article Dans Une Revue IEEE Antennas and Propagation Magazine Année : 2017

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

Marcella Bonazzoli
Frédéric Hecht
Maya de Buhan
Serguei Semenov
  • Fonction : Auteur

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.

Dates et versions

hal-01623106 , version 1 (25-10-2017)

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Citer

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, 2017, 59 (5), pp.98 - 110. ⟨10.1109/MAP.2017.2731199⟩. ⟨hal-01623106⟩

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