Enhancing the depth resolution of contactless electrical conductivity imaging

Contactless electrical conductivity imaging (CECI) collects magnetic field measurements of the induced currents from a biological subject. Because magnetic induction strength is highly dependent on the distance between the source and the measurements, information about the deeper conductivity variations are highly vulnerable to measurement noise. In this study, a novel form of the bounded data uncertainties (BDU) algorithm is designed to improve the depth resolution of CECI. The column weighted bounded data uncertainties (CWBDU) algorithm defines column-specific regularization parameters for ill-posed problems, without increasing the computational complexity. The performance of CWBDU is tested on a simulated CECI setup, and results are presented with Tikhonov regularization, Moore Penrose inversion, and BDU results for comparison. The mean square error of the images are at least 1.25 dB better than the mentioned algorithm results for all our test cases. In general, CWBDU seems to be promising tool for other ill-posed problems, such as electrical impedance tomography, and eddy current imaging.