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

TL;DR

This paper explores microwave tomographic imaging for detecting cerebrovascular accidents, emphasizing the use of high-performance computing and parallel algorithms to achieve rapid, accurate reconstructions of brain tissue properties.

Contribution

It introduces a computational framework combining domain decomposition methods and high-performance computing for efficient microwave imaging of cerebrovascular accidents.

Findings

Demonstrates feasibility of rapid, accurate brain imaging using microwave tomography.

Develops parallel algorithms for solving Maxwell's equations efficiently.

Shows potential for improved diagnosis speed and accuracy.

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.