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Article Dans Une Revue Parallel Computing Année : 2019

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

Marcella Bonazzoli
Maya de Buhan
Serguei Semenov
  • Fonction : Auteur

Résumé

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.
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Dates et versions

hal-01343687 , version 1 (12-07-2016)
hal-01343687 , version 2 (21-06-2019)

Identifiants

Citer

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. Parallel Computing, 2019, 85, pp.88-97. ⟨10.1016/j.parco.2019.02.004⟩. ⟨hal-01343687v2⟩
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