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

doi:10.1109/MAP.2017.2731199

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

Mathematics And Statistics

Research output: Contribution to journal › Article › peer-review

128 Citations (Scopus)

62 Downloads (Pure)

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 language	English
Pages (from-to)	98-110
Number of pages	13
Journal	IEEE Antennas and Propagation Magazine
Volume	59
Issue number	5
Early online date	22 Aug 2017
DOIs	https://doi.org/10.1109/MAP.2017.2731199
Publication status	Published - 1 Oct 2017

Funding

This work is supported by the French National Research Agency (ANR) grant MEDIMAX (ANR-13-MONU-0012). This work was granted access to the HPC resources of TGCC at CEA under the allocations 2016-067519 and 2016-067730 made by Grand Équipement National de Calcul Intensif (GENCI). We would like to thank ANR for its support.

Keywords

cerebrovascular
brain stroke detection
microwave tomography

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10.1109/MAP.2017.2731199

Tournier-etal-IEEEAPM-2017-Numerical-modeling-and-high-speed-parallel-computingAccepted author manuscript, 2.91 MB

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Tournier, P. H., Bonazzoli, M., Dolean, V., Rapetti, F., Hecht, F., Nataf, F., Aliferis, I., El Kanfoud, I., Migliaccio, C., De Buhan, M., Darbas, M., Semenov, S., & Pichot, C. (2017). Numerical modeling and high-speed parallel computing: new perspectives on tomographic microwave imaging for brain stroke detection and monitoring. IEEE Antennas and Propagation Magazine, 59(5), 98-110. https://doi.org/10.1109/MAP.2017.2731199

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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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Tournier, PH, Bonazzoli, M, Dolean, V, Rapetti, F, Hecht, F, Nataf, F, Aliferis, I, El Kanfoud, I, Migliaccio, C, De Buhan, M, Darbas, M, Semenov, S & Pichot, C 2017, 'Numerical modeling and high-speed parallel computing: new perspectives on tomographic microwave imaging for brain stroke detection and monitoring', IEEE Antennas and Propagation Magazine, vol. 59, no. 5, pp. 98-110. https://doi.org/10.1109/MAP.2017.2731199

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AU - Tournier, Pierre Henri

AU - Bonazzoli, Marcella

AU - Dolean, Victorita

AU - Rapetti, Francesca

AU - Hecht, Frederic

AU - Nataf, Frederic

AU - Aliferis, Iannis

AU - El Kanfoud, Ibtissam

AU - Migliaccio, Claire

AU - De Buhan, Maya

AU - Darbas, Marion

AU - Semenov, Serguei

AU - Pichot, Christian

N1 - © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2017/10/1

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N2 - 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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Numerical modeling and high-speed parallel computing: new perspectives on tomographic microwave imaging for brain stroke detection and monitoring

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