Electrical impedance tomography with an optimized calculable square sensor.

Electrical impedance tomography is a technique that reconstructs the medium distribution in a region of interest through electrical measurements on its boundary. In this paper, an optimized square sensor was designed for electrical impedance tomography in order to obtain maximum information over the cross section of interest, e.g., circulating fluidized beds, in the sense of Shannon information entropy. An analytical model of the sensor was obtained using the conformal transformation. The model indicates that the square sensor possesses calculable property, which allows the calculation of standard values of the sensor directly from a single dimensional measurement that can be made traceable to the SI unit of length. Based on the model, the sensitivity maps and electrical field lines can be calculated in less than a second. Two model based algorithms for image reconstruction, i.e., back projection algorithm based on electrical field lines and iterative Lavrentiev regularization algorithm based on the sensitivity map, were introduced. Simulated results and experimental results validate the feasibility of the algorithms.