Imaging Error from Mislocation of Surface Sensors: Sensitivity, Detection, and Estimate Correction, with Application to EIT

The accuracy of the inverse solutions obtained by imaging modalities which use surface measurements can be significantly reduced by even small modeling errors. One important type of modeling error is inaccuracy in locating the sensors. In this paper, we first report on the sensitivity to sensor mislocation of a known-geometry inverse solution both through simulation and by calculating the Cramer-Rao lower bound on the estimate variance. We then introduce a method based on a likelihood ratio hypothesis test to use the statistical behavior of the residuals to identify potential sensor mislocation. Upon detection of sensor location error, we then propose an approach, based on principal component analysis of the residuals, to reduce the sensitivity of the reconstruction algorithm to this kind of modeling error. We show the simulation results using a method, introduced in our previous work, for electrical impedance tomography based on boundary element methods