Microwave tomographic imaging of cerebrovascular accidents by using high-performance computing

Tournier, P.-H. and Aliferis, I. and Bonazzoli, M. and Buhan, M. de and Darbas, M. and Dolean, V. and Hecht, F. and Jolivet, P. and El Kanfoud, I. and Migliaccio, C. and Nataf, F. and Pichot, Ch. and Semenov, S. (2019) Microwave tomographic imaging of cerebrovascular accidents by using high-performance computing. Parallel Computing, 85. pp. 88-97. ISSN 0167-8191 (https://doi.org/10.1016/j.parco.2019.02.004)

[thumbnail of Tournier-etal-PC2019-Microwave-tomographic-imaging-of-cerebrovascular-accidents]
Preview
 Text. Filename: Tournier_etal_PC2019_Microwave_tomographic_imaging_of_cerebrovascular_accidents.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (1MB)| Preview

Abstract

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.

CORE (COnnecting REpositories)