ESTIMATION OF TISSUE RESISTIVITIES FROM MULTIPLE-ELECTRODE IMPEDANCE MEASUREMENTS

In order to measure in vivo resistivity of tissues in the thorax, the possibility of combining anatomical data extracted from high-resolutio n images with multiple-electrode impedance measurements, a priori know ledge of the range of tissue resistivities, and a priori data on the i nstrumentation noise is assessed in this study. A statistically constr ained minimum-mean-square error estimator (MIMSEE) that minimizes the effects of linearization errors and instrumentation noise is developed and compared to the conventional least-squares error estimator (LSEE) . The MIMSEE requires a priori signal and noise information. The stati stical constraint signal information was obtained from a priori knowle dge of the physiologically allowed range of regional resistivities. Th e noise constraint information was obtained from a priori knowledge of the linearization error and the instrumentation noise. The torso pote ntials were simulated by employing a three-dimensional canine torso mo del. The model consists of four different conductivity regions: heart, right lung, left lung, and body. It is demonstrated that the statisti cally constrained MIMSEE performs significantly better than the LSEE i n determining resistivities, The results based on the torso model indi cate that regional resistivities can be estimated to within 40% accura cy of their true values by utilizing a statistically constrained MIMSE E, even if the instrumentation noise is comparable to the measured tor so potentials. The errors obtained using the LSEE with the same linear ized transfer function and level of instrumentation noise were about f ive times larger than those obtained using the MIMSEE. For larger meas urement errors the MIMSEE performs even better when compared to the LS EE.