Microwave Tomographic Imaging of Cerebrovascular Accidents by Using High-Performance Computing

Pierre-Henri Tournier (1, 2) , Ioannis Aliferis (3) , Marcella Bonazzoli (4) , Maya de Buhan (5) , Marion Darbas (6) , Victorita Dolean (4, 7) , Frédéric Hecht (1, 2) , Pierre Jolivet (8) , Ibtissam El Kanfoud (3) , Claire Migliaccio (3) , Frédéric Nataf (1, 2) , Christian Pichot (3) , Serguei Semenov (9)

1 LJLL - Laboratoire Jacques-Louis Lions
2 ALPINES - Algorithms and parallel tools for integrated numerical simulations
3 LEAT - Laboratoire d'Electronique, Antennes et Télécommunications
4 LJAD - Laboratoire Jean Alexandre Dieudonné
5 MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5
6 LAMFA - Laboratoire Amiénois de Mathématique Fondamentale et Appliquée
7 Department of Mathematics and Statistics
8 IRIT - Institut de recherche en informatique de Toulouse
9 EMTensor GmbH

The motivation of this work is the detection of cerebrovascular accidents by microwave tomographic imaging. This requires the solution of an inverse problem relying on a minimization algorithm (for example, gradient-based), where successive iterations consist in repeated solutions of a direct problem. The reconstruction algorithm is extremely computationally intensive and makes use of efficient parallel algorithms and high-performance computing. The feasibility of this type of imaging is conditioned on one hand by an accurate reconstruction of the material properties of the propagation medium and on the other hand by a considerable reduction in simulation time. Fulfilling these two requirements will enable a very rapid and accurate diagnosis. From the mathematical and numerical point of view, this means solving Maxwell's equations in time-harmonic regime by appropriate domain decomposition methods, which are naturally adapted to parallel architectures.

Domaines: Analyse numérique [math.NA]

Pierre-Henri Tournier, Ioannis Aliferis, Marcella Bonazzoli, Maya de Buhan, Marion Darbas, et al.. Microwave Tomographic Imaging of Cerebrovascular Accidents by Using High-Performance Computing. 2016. ⟨hal-01343687v1⟩