Registration and Analysis of Developmental Image Sequences

Mapping images into the same anatomical coordinate system via image registration is a fundamental step when studying physiological processes, such as brain development. Standard registration methods are applicable when biological structures are mapped to the same anatomy and their appearance remains constant across the images or changes spatially uniformly. However, image sequences of animal or human development often do not follow these assumptions, and thus standard registration methods are unsuited for their analysis. In response, this dissertation tackles the problems of i) registering developmental image sequences with spatially non-uniform appearance change and ii) reconstructing a coherent 3D volume from serially sectioned images with non-matching anatomies between the sections. There are three major contributions presented in this dissertation. First, I develop a similarity metric that incorporates a time-dependent appearance model into the registration framework. The proposed metric allows for longitudinal image registration in the presence of spatially non-uniform appearance change over time—a common medical imaging problem for longitudinal magnetic resonance images of the neonatal brain. Next, a method is introduced for registering longitudinal developmental datasets with missing time points using an appearance atlas built from a population. The proposed method is applied to a longitudinal study of young macaque monkeys with incomplete image sequences. The final contribution is a template-free registration method to reconstruct images of serially sectioned biological samples into a coherent 3D volume. The method is applied to confocal fluorescence microscopy images of serially sectioned embryonic mouse brains.Doctor of Philosoph

Longitudinal Image Registration With Temporally-Dependent Image Similarity Measure

Longitudinal imaging studies are frequently used to investigate temporal changes in brain morphology and often require spatial correspondence between images achieved through image registration. Beside morphological changes, image intensity may also change over time, for example when studying brain maturation. However, such intensity changes are not accounted for in image similarity measures for standard image registration methods. Hence, (i) local similarity measures, (ii) methods estimating intensity transformations between images, and (iii) metamorphosis approaches have been developed to either achieve robustness with respect to intensity changes or to simultaneously capture spatial and intensity changes. For these methods, longitudinal intensity changes are not explicitly modeled and images are treated as independent static samples. Here, we propose a model-based image similarity measure for longitudinal image registration that estimates a temporal model of intensity change using all available images simultaneously

Low Mach-number collisionless electrostatic shocks and associated ion acceleration

The existence and properties of low Mach-number electrostatic collisionless shocks are investigated with a semi-analytical solution for the shock structure. We show that the properties of the shock obtained in the semi-analytical model can be well reproduced in fully kinetic Eulerian Vlasov-Poisson simulations, where the shock is generated by the decay of an initial density discontinuity. Using this semi-analytical model, we study the effect of electron-to-ion temperature ratio and presence of impurities on both the maximum shock potential and Mach number. We find that even a small amount of impurities can influence the shock properties significantly, including the reflected light ion fraction, which can change several orders of magnitude. Electrostatic shocks in heavy ion plasmas reflect most of the hydrogen impurity ions