An Introduction to Electrical Impedance Tomography

نویسنده

  • Liliana Borcea
چکیده

The inverse problem of Electrical Impedance Tomography (EIT) is that of imaging a spatially varying, complex admittivity function γ(x, ω) in a domain Ω ⊂ I R (n = 2 or 3), given measurements of low frequency ω alternating electrical currents and potentials at the boundary ∂Ω. The real and imaginary parts of admittivity γ(x, ω) are the electrical conductivity σ(x) and ω (x), respectively, where (x) is the permittivity (dielectric constant). In mathematical terms, the EIT problem is that of recovering coefficients σ(x) and (x) in a system of elliptic partial differential equations, defined for x ∈ Ω, given knowledge of the Cauchy data, i.e. the Neumann to Dirichlet or Dirichlet to Neumann maps. The EIT problem has important applications in fields such as medical imaging, nondestructive testing, environmental cleaning, geophysics, etc. and, over the last two decades, it has been the topic of many theoretical and numerical studies. In spite of the rich literature on EIT, there are important, outstanding questions that do not have satisfactory answers, so far. Examples of such questions are: improving the stability of imaging algorithms, the resolution and reliability of images of γ(x, ω), increasing the speed of inversion algorithms so γ(x, ω) can be imaged in real time, etc. This set of lectures gives a brief review of theoretical and numerical results available for the EIT problem. A list of topics is: 1. Formulation of the EIT problem. Definition of the Neumann to Dirichlet and Dirichlet to Neumann maps. Variational principles. Regularity of the forward map. 2. Uniqueness of the solution of the EIT problem. Calderon’s proof of injectivity of the linearized map. Sylvester and Uhlmann’s 3D uniqueness proof. 3. Instability of the EIT problem. Regularization. Alessandrini’s stability estimate for regular conductivities. 4. Imaging algorithms. Linearization (Born approximation). Nonlinear, Newton-type imaging algorithms. Variational approaches: equation-error methods and variational constraints on the unknown γ. Layer stripping. Multigrid reconstructions of γ. 5. High contrast imaging. 6. Optimal grids.

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تاریخ انتشار 2003