Numerical modeling and high-speed parallel computing: new perspectives on tomographic microwave imaging for brain stroke detection and monitoring

Pierre Henri Tournier, Marcella Bonazzoli, Victorita Dolean, Francesca Rapetti, Frederic Hecht, Frederic Nataf, Iannis Aliferis, Ibtissam El Kanfoud, Claire Migliaccio, Maya De Buhan, Marion Darbas, Serguei Semenov, Christian Pichot

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)98-110
Number of pages13
JournalIEEE Antennas and Propagation Magazine
Volume59
Issue number5
Early online date22 Aug 2017
DOIs
Publication statusPublished - 1 Oct 2017

Keywords

  • cerebrovascular
  • brain stroke detection
  • microwave tomography

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