3.9 Article

Detection of Simulated Brain Strokes Using Microwave Tomography

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JERM.2019.2921076

Keywords

Microwave imaging; biomedical imaging; inverse problems; mathematical programming; optimizationmethods; signal reconstruction; dielectric constant; medical image processing; parallel programming; brain stroke imaging; domain-specific language; gradient based minimization algorithm; regularization methods; total variation; hemorrhagic brain stroke detection; high-speed parallel computing; iterative microwave tomographic imaging; massively parallel computing; numerical modeling; open source FreeFem plus plus solver; whole-microwave measurement system; brain modeling; computational modeling; tomography

Funding

  1. French National Research Agency
  2. EPSRC [EP/S004017/1] Funding Source: UKRI

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Brain strokes are one of the leading causes of disability and mortality in adults in developed countries. Ischemic stroke (85% of total cases) and hemorrhagic stroke (15%) must be treated with opposing therapies, and thus, the nature of the stroke must be determined quickly in order to apply the appropriate treatment. Recent studies in biomedical imaging have shown that strokes produce variations in the complex electric permittivity of brain tissues, which can be detected by means of microwave tomography. Here, we present some synthetic results obtained with an experimental microwave tomography-based portable system for the early detection andmonitoring of brain strokes. The determination of electric permittivity first requires the solution of a coupled forward-inverse problem. We make use of massive parallel computation from domain decomposition method and regularization techniques for optimization methods. Synthetic data are obtained with electromagnetic simulations corrupted by noise, which have been derived from measurements errors of the experimental imaging system. Results demonstrate the possibility to detect hemorrhagic strokes with microwave systems when applying the proposed reconstruction algorithm with edge preserving regularization.

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