Nové typy a principy optimalizace digitálního zpracování obrazů v EIT ; New Optimization Algorithms for a Digital Image Reconstruction in EIT

This doctoral thesis proposes a new algorithm for the reconstruction of impedance images in monitored objects. The algorithm eliminates the spatial resolution problems present in existing reconstruction methods, and, with respect to the monitored objects, it exploits both the partial knowledge of configuration and the material composition. The discussed novel method is designed to recognize certain significant fields of interest, such as material defects or blood clots and tumors in biological images. The actual reconstruction process comprises two phases; while the former stage is focused on industry-related images, with the aim to detect defects in conductive materials, the latter one concentrates on biomedical applications. The thesis also presents a description of the numerical model used to test the algorithm. The testing procedure was centred on the resulting impedivity value, influence of the regularization parameter, initial value of the numerical model impedivity, and effect exerted by noise on the voltage electrodes upon the overall reconstruction results. Another issue analyzed herein is the possibility of reconstructing impedance images from components of the magnetic flux density measured outside the investigated object. The given magnetic field is generated by a current passing through the object. The created algorithm for the reconstruction of impedance images is modeled on the proposed algorithm for EIT-based reconstruction of impedance images from voltage. The algoritm was tested for stability, influence of the regularization parameter, and initial conductivity. From the general perspective, the thesis describes the methodology for both magnetic field measurement via NMR and processing of the obtained data.