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Abstract:

X-ray sources are polychromatic. Ignoring this fact when performing reconstruction leads to artifacts, such as cupping and streaking, in reconstructed images. We first propose a new model parameterization that allows for blind correction of these artifacts and then develop reconstruction algorithms based on this parameterization.

Here, blind correction means that we do not know
- incident spectrum (which is an X-ray machine characteristic) and
- mass attenuation (inspected material).

In this dissertation, we propose the first, to the best of our knowledge, PCA based algorithm to noninvasively recognize and classify different temporal stages of brain tumors given a large time series of MRI images. We propose an algorithm that addresses the challenging task of classifying stage of tumor over period of time while the tumor is being treated with VB-111 virotherapy. Our approach treats stage tumor recognition as a two-dimensional recognition problem. Detecting the stage of the tumor is a crucial prognosis factor for predicting the progression of cancer and patient survival.

Phase synchrony measures computed on electrophysiological signals play an important role in the assessment of cognitive and sensory processes. However, due to the effects of volume conduction false synchronization values may arise between time series. Measures such as the imaginary part of coherence (ImC), phase-lag index (PLI) and an enhanced version of it, the weighted PLI (WPLI) have been proposed in order to attenuate the effects of volume conduction.