TY - JOUR
T1 - Numerical modeling and high-speed parallel computing
T2 - new perspectives on tomographic microwave imaging for brain stroke detection and monitoring
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
Y1 - 2017/10/1
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.
AB - 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.
KW - cerebrovascular
KW - brain stroke detection
KW - microwave tomography
UR - http://www.scopus.com/inward/record.url?scp=85028515210&partnerID=8YFLogxK
U2 - 10.1109/MAP.2017.2731199
DO - 10.1109/MAP.2017.2731199
M3 - Article
AN - SCOPUS:85028515210
SN - 1045-9243
VL - 59
SP - 98
EP - 110
JO - IEEE Antennas and Propagation Magazine
JF - IEEE Antennas and Propagation Magazine
IS - 5
ER -