CONVERGENCE AND STABILITY ASSESSMENT OF NEWTON-KANTOROVICH RECONSTRUCTION ALGORITHMS FOR MICROWAVE TOMOGRAPHY

For newly developed iterative Newton-Kantorovitch reconstruction techn iques, the quality of the final image depends on both experimental and model noise. Experimental noise is inherent to any experimental acqui sition scheme, while model noise refers to the accuracy of the numeric al model, used in the reconstruction process, to reproduce the experim ental setup, This paper provides a systematic assessment of the major sources of experimental and model noise on the quality of the final im age. This assessment is conducted from experimental data obtained with a microwave circular scanner operating at 2.33 GHz, Targets to be ima ged include realistic biological structures, such as a human forearm, as well as calibrated samples for the sake of accuracy evaluation, The results provide a quantitative estimation of the effect of experiment al factors, such as temperature of the immersion medium, frequency, si gnal-to-noise ratio, and various numerical parameters.