COMPARISON OF DIFFERENT TECHNIQUES FOR ELECTRICAL IMPEDANCE TOMOGRAPHY RECONSTRUCTION USING SIMULATED ANNEALING AND GPU PARALLELIZATION

Electrical Impedance Tomography (EIT) is an imaging technique that attempts to reconstruct the conductivity distribution inside an object from electrical currents and potentials applied and measured at its surface. The EIT reconstruction problem is approached as an optimization problem. This optimization problem can be solved using Simulated Annealing (SA), but at a high computational cost. To reduce the computational load, it is possible to use an incomplete evaluation of the objective function. Two objective functions are analyzed and compared: Euclidian distance and least square minimization. The Euclidian distance minimization showed to present an outside-in behavior, determining the impedance of the external elements first, similar to a layer striping algorithm. It also presents the impact of using GPU for parallelizing matrixvector multiplication. Results with experimental data are presented.

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