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Visual Abstract, Mild traumatic brain injury (mTBI) is the most common form of TBI with 10–25% of the patients experiencing long-lasting symptoms. The potential of diffusion tensor imaging (DTI) for evaluating microstructural damage after TBI is widely recognized, but the interpretation of DTI changes and their relationship with the underlying tissue damage is unclear. We studied how both axonal damage and gliosis contribute to DTI alterations after mTBI. We induced mTBI using the lateral fluid percussion (LFP) injury model in adult male Sprague Dawley rats and scanned them at 3 and 28 d post-mTBI. To characterize the DTI findings in the tissue, we assessed the histology by performing structure tensor (ST)-based analysis and cell counting on myelin-stained and Nissl-stained sections, respectively. In particular, we studied the contribution of two tissue components, myelinated axons and cellularity, to the DTI changes. Fractional anisotropy (FA), mean diffusivity (MD), and axial diffusivity (AD) were decreased in both white and gray matter areas in the acute phase post-mTBI, mainly at the primary lesion site. In the subacute phase, FA and AD were decreased in the white matter, external capsule, corpus callosum, and internal capsule. Our quantitative histologic assessment revealed axonal damage and gliosis throughout the brain in both white and gray matter, consistent with the FA and AD changes. Our findings suggest that the usefulness of in vivo DTI is limited in its detection of secondary damage distal to the primary lesion, while at the lesion site, DTI detected progressive microstructural damage in the white and gray matter after mTBI.

Tracing the entirety of ultrastructures in large three-dimensional electron microscopy (3D-EM) images of the brain tissue requires automated segmentation techniques. Current segmentation techniques use deep convolutional neural networks (DCNNs) and rely on high-contrast cellular membranes and high-resolution EM volumes. On the other hand, segmenting low-resolution, large EM volumes requires methods to account for severe membrane discontinuities inescapable. Therefore, we developed DeepACSON, which performs DCNN-based semantic segmentation and shape-decomposition-based instance segmentation. DeepACSON instance segmentation uses the tubularity of myelinated axons and decomposes under-segmented myelinated axons into their constituent axons. We applied DeepACSON to ten EM volumes of rats after sham-operation or traumatic brain injury, segmenting hundreds of thousands of long-span myelinated axons, thousands of cell nuclei, and millions of mitochondria with excellent evaluation scores. DeepACSON quantified the morphology and spatial aspects of white matter ultrastructures, capturing nanoscopic morphological alterations five months after the injury., With DeepACSON, Abdollahzadeh et al. combines existing deep learning-based methods for semantic segmentation and a novel shape decomposition technique for the instance segmentation. The pipeline is used to segment low-resolution 3D-EM datasets allowing quantification of white matter morphology in large fields-of-view.

The phenomenological theory of sedimentation-thickening processes predicts the settling behavior of a flocculated suspension in dependence of two functions, the batch flux density function and the effective solid stress. These functions were determined using batch settling tests. The governing equations for sedimentation were then solved numerically for these functions and the predictions were compared to the experimental results from pilot scale thickener tests. Firstly, the continuous tests were performed in the plexiglass pilot thickener at different feed flow rates and discharge rates and the solid volume fraction of discharge, the bed height and the time were recorded for each condition. These tests were also simulated and it was observed that there is a good agreement between the results of continuous tests and the results of dynamic simulation. Secondly, the discontinuous tests were performed in the plexiglass pilot thickener at different feed flow rates with a discharge rate of zero. The bed formation rate was determined for each condition. These tests were also simulated and it was observed that there is a good agreement between the results of discontinuous tests and the results of simulation.

Electron tomography (ET) is a technique to reveal the interior structures of organic-subcellular macro molecules- and inorganic materials from their 2D cross sectional transmission electron microscope (TEM) projections. However, restricted radiation dose due to specimen damage and blind region of angular sampling as a result of physical constraints deteriorate the quality of the resultant tomograms. Typically, electron tomograms suffer from low signal to noise ratio (SNR) and elongation artifact in the direction of electron radiation. Different studies propose methods to tackle the constraints of ET in the data acquisition stage. This thesis is a comparative study among different data acquisition models by analyzing the resultant tomogram of each method quantitatively. We implement each model with a TEM simulator and compare the tomograms by their root mean square (RMS) and resolution. Results of TEM settings indicate that 1) reducing the acceleration voltage and increasing the defocus value intensifies the contrast. 2) Diminishing the objective diaphragm size reduces the brightness of the projections. Comparing data acquisition models states that 1) cosine model of dose distribution homogenizes the SNR of sinograms and compared to the conventional methods enhances the resolution of the tomograms. 2) Employing Saxton model for angular sampling boosts the resolution and declines the elongation artifact. 3) Combination of the cosine method of dose distribution and Saxton's model promotes the resolution, RMS value and elongation artifact significantly: resolution enhanced 1.81 times compared to the constant dose and angle distribution models in Z-direction. To conclude, emphasis on the SNR and sampling frequency of highly tilted angles outperforms the conventional data acquisition approaches qualitatively and quantitatively.