At the Gesellschaft für Schwerionenforschung in Darmstadt a therapy unit for heavy ion cancer treatment has been established in collaboration with the Deutsches Krebsforschungszentrum Heidelberg, the Radiologische Universitätsklinik Heidelberg and the Forschungszentrum Rossendorf. For quality assurance the dual-head positron camera BASTEI (Beta Activity meaSurements at the Therapy with Energetic Ions) has been integrated into this facility. It measures ß+-activity distributions generated via nuclear fragmentation reactions within the target volume. BASTEI has about 4 million coincidence channels. The emission data are acquired in a 3D regime and stored in a list mode data format. Typically counting statstics is two to three orders of magnitude lower than those of typical PET-scans in nuclear medicine. Two iterative 3D reconstruction algorithms based on ISRA (Image Space Reconstruction Algorithm) and MLEM (Maximum Likelihood Expectation Maximization), respectively, have been adapted to this imaging geometry. The major advantage of the developed approaches are run-time Monte-Carlo simulations which are used to calculate the transition matrix. The influences of detector sensitivity variations, randoms, activity from outside of the field of view and attenuation are corrected for the individual coincidence channels. Performance studies show, that the implementation based on MLEM is the algorithm of merit. Since 1997 it has been applied sucessfully to patient data. The localization of distal and lateral gradients of the ß+-activity distribution is guaranteed in the longitudinal sections. Out of the longitudinal sections the lateral gradients of the ß+-activity distribution should be interpreted using a priori knowledge.

Optical Coherence Tomography (OCT) is a medical imaging technique which distinguishes itself by acquiring microscopic resolution images in-vivo at millimeter scale fields of view. The resulting in images are not only high-resolution, but often multi-dimensional to capture 3-D biological structures or temporal processes. The nature of multi-dimensional data presents a unique set of challenges to the OCT user that include acquiring, storing, and handling very large datasets, visualizing and understanding the data, and processing and analyzing the data. In this dissertation, three of these challenges are explored in depth: sub-resolution temporal analysis, 3-D modeling of fiber structures, and compressed sensing of large, multi-dimensional datasets. Exploration of these problems is followed by proposed solutions and demonstrations which rely on tools from multiple research areas including digital image filtering, image de-noising, and sparse representation theory. Combining approaches from these fields, advanced solutions were developed to produce new and groundbreaking results. High-resolution video data showing cilia motion in unprecedented detail and scale was produced. An image processing method was used to create the first 3-D fiber model of uterine tissue from OCT images. Finally, a compressed sensing approach was developed which we show to guarantee high accuracy image recovery of more complicated, clinically relevant, samples than had been previously demonstrated. The culmination of these methods represents a step forward in OCT image analysis, showing that these cutting edge tools can also be applied to OCT data and in the future be employed in a clinical setting.

Die moderne multimodale Computertomografie (CT) beinhaltet das Schichtröntgen des Gehirns (native CT), die Darstellung der hirnversorgenden Arterien (CT-Angiografie) und die Messung der Hirndurchblutung (CT-Perfusion). Mit Hilfe dieser Untersuchungstechnik kann bei Patienten mit akutem Schlaganfall rasch die Ursache der plötzlich eingesetzten Symptome beleuchtet werden: Liegt eine Gefäßobstruktion oder eine Blutung in das Gehirn vor? Wie ausgedehnt ist die Durchblutungsstörung und wie viel Hirngewebe ist bereits beschädigt bzw. vom Untergang bedroht? Anhand dieser Informationen kann sofort eine spezifische Therapie eingeleitet werden, die es ermöglicht, die Patienten vor dauerhafter schwerer Behinderung zu bewahren bzw. die Prognose schon früh abzuschätzen. / Computed tomography (CT), including CT perfusion imaging and CT angiography, has the capacity to assess stroke pathology on a functional and morphological level and can thus provide important information about patients with acute stroke. It excludes brain haemorrhage, assesses the extent of perfusion deficit, the extent of ischemic damage, and the site and type of arterial obstruction. Ischemic brain tissue below the blood flow level of structural integrity takes up water immediately and causes a decrease in x-ray attenuation. Computed tomography thus has the specific advantage of being able to identify the brain tissue which is irreversibly injured. If CT can exclude major ischemic damage in acute stroke patients, reperfusion strategies may rescue brain function and prevent disability.

Pores provide important hotspots for chemical and biological processes in soils. Earthworm burrows affect the macropore structure and their actions may create new preferential pathways for water and gas flow within soils. This, in turn, indirectly affect plants, nutrient cycling, hydraulic conductivity, gas exchange, and soil organisms. While the effects of invasive earthworms on soil properties has been well-documented in temperate and boreal ecosystems, we know little how these organism may affect tundra soils. In this study, I assessed how the three-dimensional network of soil-macropores are affected by earthworm species (Aporrectodea sp. and Lumbricus sp). I hypothesized: i) that earthworms increase the frequency of macropores with a likely biological origin (biopores); ii) effects of biopores are dependent on tundra vegetation type (meadow or heath); and iii) the macropore network properties are altered by earthworms.  The hypotheses were tested using a common garden experiment with 48 mesocosms. The pore structure of each mesocosm was analyzed using X-ray CT tomography. I found that biopores increased in the tundra from on 0.05 ±0.01 % (mean ± standard deviation) in the control to about 0.59 ± 0.07 % in the earthworm treatments. However, in contrast to my second hypothesis, I found no vegetation dependent effect. Interestingly, I found that earthworms decreased the complexity and directionality of macropores. My findings strongly indicate that burrowing can severely impact the pore properties of previously uninhabited subarctic soils.

Gasoline and gasoline-like fuels (naphtha) have high volatility, which results in flash boiling spray in gasoline engines when operated at throttling or low load conditions. Flash boiling can achieve better atomization, thus benefit fuel evaporation and fuel-air mixing. However, when flash boiling occurs, spray morphology, and fuel distribution are dramatically varied from the injectors' intentional design. This difference will affect the performance of combustion and emissions. Thus it is essential to investigate the spray collapse phenomenon regarding varied conditions. The currently developing gasoline compression ignition (GCI) engines, also has throttled stoichiometric spark ignition operation mode, which inevitably has flash boiling possibility. However, there is a lack of research on flash boiling spray with a GCI injector, which has a large designed cone angle. This work aims to understand the spray collapse phenomenon and fill the gap in GCI flash boiling spray. Simultaneous side-view diffused back illumination (DBI) and front-view mie-scattering are used to capture the liquid spray development. Simultaneous shadowgraph from side and front view are used for recording the liquid+vapor phase spray development. Criteria for distinguishing different spray regimes have been established from these results. It shows this GCI injector is more resistant to collapse than the other conventional gasoline direct injection (GDI) injectors reported in the literature. A combination of DBI and space-time tomographic algorithm is validated in this work, achieving 3D reconstruction of the spray volume development from non-flashing to collapsed spray regime at low cost. The 3D results help elucidate the spray collapse procedure and provide validation data for CFD simulation. Structured laser illumination planar imaging (SLIPI) is firstly implemented in flash boiling spray study in this work to suppress the multiple scattering effect. Reconstructed 3D results from slice sweeping by SLIPI methods exposes the hollow structure in the spray's collapsed central jet, which has not been reported previously by other methods. Different spray motion types are summarized for the transitional and collapsed spray regime from the SLIPI slice and confirmed by the particle image velocimetry (PIV) technique.

Cryogenic electron tomography provides visualization of cellular complexes in situ, allowing a further understanding of cellular function. However, the projection images from this technique present a meager signal-to-noise ratio due to the limited electron dose, and the lack of projections at high tilt angles produces the 'missing-wedge' problem in the Fourier domain. These limitations in the projection data prevent traditional reconstruction techniques from achieving good reconstructions. Multiple strategies have been proposed to deal with the noise and the artifacts arising from the 'missing-wedge’ problem. For example, manually selecting subtomograms of identical structures and averaging them (subtogram averaging), data-driven approaches that intend to perform subtogram averaging automatically, and various methods for denoising tilt-series before reconstruction or denoising the volumes after reconstruction. Most of these approaches are additional pre-processing or post-processing steps independent from the reconstruction method, and the consistency of the resulting tomograms with the original projection data is lost after the modifications. We propose a GPU accelerated optimization-based reconstruction framework using proximal algorithms. Our framework integrates denoising in the reconstruction process by alternating between reconstruction and denoising, relieving the users of the need to select additional denoising algorithms and preserving the consistency between final tomograms and projection data. Thanks to the flexibility provided by proximal algorithms, various available proximal operators can be interchanged for each task, e.g., various algebraic reconstruction methods and denoising techniques. We evaluate our approach qualitatively by comparison with current reconstruction and denoising approaches, showing excellent denoising capabilities and superior visual quality of the reconstructed tomograms. We quantitatively evaluate the methods with a recently proposed synthetic dataset for scanning transmission electron microscopy, achieving superior reconstruction quality for a noisy and angle-limited synthetic dataset.

A vascular corrosion cast of an entire mouse kidney was scanned with a modular multiresolution X-ray nanotomography system. Using an isotropic voxel pitch of 0.5 μm, capillary systems such as the vasa recta, peritubular capillaries and glomeruli were clearly resolved. This represents a considerable improvement over corrosion casts scanned with microcomputed tomography systems. The resolving power of this system was clearly demonstrated by the unique observation of a dense, subcapsular mat of capillaries enveloping the entire outer surface of the cortical region. Resolution of glomerular capillaries was comparable to similar models derived from laser scanning confocal microscopy. The high-resolution, large field of view and the three-dimensional nature of the resulting data opens new possibilities for the use of corrosion casting in research.

Medical imaging is widely used in current healthcare and research settings for various purposes such as diagnosis, treatment options, patient monitoring, longitudinal studies, etc. The two most commonly used imaging modalities in the United States are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Raw images acquired via CT or MRI need to undergo a variety of processing steps prior to being used for the purposes explained above. These processing steps include quality control, noise reduction, anatomical segmentation, tissue classification, etc. However, since medical images often include millions of voxels (smallest 3D units in the image containing information) it is extremely challenging to process them manually by relying on visual inspection and the experience of trained clinicians. In light of this, the field of medical imaging is seeking ways to automate data processing. With the impressive performance of Artificial Intelligence (AI) in the field of Computer Vision, researchers in the medical imaging community have shown increasing interest in utilizing this powerful tool to automate the task of processing medical imaging data. Despite AI’s significant contributions to the medical imaging field, large cohorts of data still remain without optimized and robust AI-based tools to process images efficiently and accurately. This thesis focuses on exploiting large cohorts of CT and MRI data to design AI-based methods for processing medical images using weakly-supervised and supervised learning strategies, as well as mathematical (and/or statistical) modeling and signal processing methods. In particular, we address four image processing problems in this thesis. Namely: 1) We propose a weakly-supervised deep learning method to automate binary quality control of diffusion MRI scans into ‘poor’ and ‘good’ quality classes; 2) We design a weakly-supervised deep learning framework to learn and detect visual patterns related to a set of different artifact categories considered in this work, in order to identify major artifact types present in dMRI volumes; 3) We develop a supervised deep learning method to classify multiple lung texture patterns with association to Emphysema disease on human lung CT scans; 4) We investigate and characterize the properties of two types of negative BOLD response elicited in human brain fMRI scans during visual stimulation using mathematical modeling and signal processing tools. Our results demonstrate that through the use of artificial intelligence and signal processing algorithms: 1) dMRI scans can be automatically categorized into two quality groups (i.e., ‘poor’ vs ‘good’) with a high classification accuracy, enabling rapid sifting of large cohorts of dMRI scans to be utilized in research or clinical settings; 2) Type of the major artifact present in ‘poor’ quality dMRI volumes can be identified robustly and automatically with high precision enabling exclusion/correction of corrupt volumes according to the artifact type contaminating them; 3) Multiple lung texture patterns related to Emphysema disease can be automatically and robustly classified across various large cohorts of CT scans enabling investigation of the disease through longitudinal studies on multiple cohorts; 4) Negative BOLD responses of different categories can be fully characterized on fMRI data collected from visual stimulation of human brain enabling researchers to better understand the human brain functionality through studying cohorts of fMRI scans.

The lithosphere of northwestern Canada recorded more than 2.5 Gy of complex tectonic evolution, from the formation of the ancient cores of the continental lithosphere such as the Slave craton to the Phanerozoic Cordilleran orogeny with substantial variations in crust and upper mantle structures that led to the concentration of natural resources (i.e., diamonds in cratons). Present-day northwestern Canada juxtaposes a thin and hot Cordilleran lithosphere to the thick and cold cratonic lithosphere, which has important implications for regional geodynamics. Recently, seismic station coverage has drastically increased across northwestern Canada, allowing the development of seismic tomography models and other passive-source seismic methods at high resolution in order to investigate the tectonic evolution and dynamics of the lithosphere in this region. The P- and S-wave upper mantle structures of northwestern Canada reveal that the distribution of kimberlite fields in the Slave craton correlates with the margin of fast and slow seismic mantle anomalies, which could delineate weak zones in the lithosphere. Based on our tomographic models we identify two high-velocity seismic anomalies straddling the arcuate Cordillera Deformation Front that have controlled its regional deformation, including a newly identified Mackenzie craton characterized by high seismic velocities extending from the lower crust to the upper mantle to the north of the Mackenzie Mountains. Furthermore, our P-wave tomography model shows sharp velocity contrasts beneath the surface trace of the Tintina Fault. Estimates of seismic anisotropy show a progressive rotation of fast-axis directions when approaching the fault zone. Together, they provide seismic evidence for the trans-lithospheric nature of the Tintina Fault. We further propose that the Tintina Fault has chiseled off small pieces of the Laurentian craton between the Late Cretaceous and the Eocene, which would imply that large lithospheric-scale shear zones are able to cut through small pieces of refractory cratonic mantle and transport them over several hundred kilometers.

In the present work atom probe tomography (APT) was applied to analyze thin films used in semiconductor industry to investigate the capability of atom probe tomography as well as the dopant redistribution in thin silicide layer stacks. Different titanium silicide layer stacks are investigated and titanium diboride precipitates are identified by APT. Arsenic grain boundary segregation is verified by APT in cobalt silicide layer stacks. Furthermore APT measurements are compared to commonly used methods such as TEM and SIMS and found in good agreement. Each method exhibits its own advantages depending on the sample and the question. Atom probe tomography offers some unique features enabling three-dimensional analysis on the nanometer scale as shown on the mentioned thin film layer stacks.