Approximation of full-boundary data from partial-boundary electrode measurements

Abstract

Measurements on a subset of the boundary are common in electrical impedance tomography, especially any electrode model can be interpreted as a partial boundary problem. The information obtained is different to full-boundary measurements as modeled by the ideal continuum model. In this study we discuss an approach to approximate full-boundary data from partial-boundary measurements that is based on the knowledge of the involved projections. The approximate full-boundary data can then be obtained as the solution of a suitable optimization problem on the coefficients of the Neumann-to-Dirichlet map. By this procedure we are able to improve the reconstruction quality of continuum model based algorithms, in particular we present the effectiveness with a D-bar method. Reconstructions are presented for noisy simulated and real measurement data.

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Cited by in corpus (2)

• Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks
• 3D EIT Reconstructions from Electrode Data using Direct Inversion D-bar and Calderon Methods