>Magister Scientiae - MSc / Nanomedicines are loosely defined as medicines that seek to apply nanotechnology. Currently, nanomedicines are available for clinical use, including treatments for cancer, high cholesterol, hepatitis, COVID-19 vaccination, among other uses (Patra et al., 2018; Gao et al., 2021). Most of the nanomedicines meet the definition of medicines according to various national legislations. Consequently, these products are regulated as medicines. Nanomedicines present major differences in biological details and increased complexity of clinical use. They integrate different technology subsets from therapeutics to imaging and integrated non-invasive diagnosis (Gaspar, 2007). These complexities require extra regulatory effort.

This thesis tackles the challenge of registering modern to historic images in the context of urban rephotography. It aims at automatically identifying stable image features in scenes, which have been exposed to medium to tremendous changes across the years. Instead, the related field of location recognition mainly focuses on illumination and seasonal changes. This work illustrates that common feature descriptors are applicable in the context of historic and modern image matching, while local detectors are not, but most important is the choice of appropriate correspondence filters. It is verified that major structural changes are most challenging for traditional image matching approaches and the methods developed in this work are applicable to challenging image pairs beyond rephotography. Besides, features extracted from Convolutional Neural Networks (CNNs), originally trained for the task of location recognition, show high performance and should be further developed for the specific task of historic to modern image matching. At last, practical developments are presented, including an online portal for presenting and organizing rephotographs as well as an initial version of a mobile application, which supports recovering the original viewpoint of an image.

The state of Maryland has been unsuccessful in achieving its goal of registering all of its population as organ donors. The purpose of this correlational study was to understand if allowing registered donors to remain anonymous would increase donor registration rates. The theoretical foundation of this study was the theory of planned behavior. Data were collected from the Motor Vehicle Administration of Maryland and the Division of Motor Vehicle of Virginia. The data were analyzed using regression displacement, interrupted time series analysis, auto correlation analysis, and Arima Box Jenkins methodology. According to the study findings, offering the option to remain anonymous and registering to be an organ donor with no heart icon on the driver's license did not have the immediate effect of encouraging more people to register as an organ donor. Parameter estimates from an Arima autoregression analysis did suggest that the impact of the removal of the heart icon may have a delayed impact, although data availability limited attempts at further investigation.

Background: Internationally educated nurses (IENs) have become a significant source of nursing supply as a result of nursing workforce shortages, the aging population and patient acuity. However, IENs face substantial delays and barriers obtaining licensure and employment equivalent to their skills and experience. When IENs are unable to practice their profession, they experience a considerable loss of professional identity, earning potential and financial stability. The purpose of this scoping review was to identify and map key themes in the existing literature on the barriers and strategies to timely nursing registration for IENs. Methods: Arksey and O’Malley’s methodological framework was used for this study. Seven electronic databases were searched along with several grey literature sources in order to capture articles that discussed barriers and strategies to timely nursing registration for IENs. A numerical and thematic analysis was conducted to explore the scope of the literature and to present the findings. Results: After full-text screening, 38 pieces of relevant literature were selected for inclusion in the review. The majority (53%) were studies and most (42%) were qualitative. Seven key themes emerged from the thematic analysis: timely information, credential assessment, obtaining documents, language requirements, nursing registration costs, bridging programs and the nursing registration exam. Conclusion: While some IENs are able to successfully navigate the process for obtaining nursing licensure, it is clear that many IENs face obstacles at every step of the process and some never become registered as nurses. Further research is required about whether the various bridging programs and initiatives meet the needs of IENs and help them become registered as nurses. Findings from this scoping review have significant implications for nurse staffing and policies and practices to improve the strength, stability, and diversity of the nursing workforce. / Thesis / Master of Science in Nursing (MSN) / This study explores the literature on internationally educated nurses (IENs) and the difficulties they face, as well as the strategies they use to obtain nursing registration. Arksey and O’Malley’s framework was used to guide this scoping review. Seven databases were searched along with many grey literature sources in order to find articles on barriers and strategies to nursing registration for IENs. A numerical and thematic analysis was conducted to present the findings. Seven main themes emerged from the thematic analysis: timely information, credential assessment, obtaining documents, language requirements, nursing registration costs, bridging programs and the nursing registration exam. More research is required about whether the various bridging programs and initiatives meet the needs of IENs and help them become registered as nurses. Findings from this study are important for nurse staffing and policies and practices to improve the stability and diversity of the nursing workforce.

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With advancements in space technology, remote sensing applications, and computer vision, significant improvements in the data describing our planet are seen today. Researchers want to gather different kinds of data and perform data fusion techniques between them to increase our understanding of the world. Two such data types are Electro-Optical images and Synthetic Aperture Radar images. For data fusion, the images need to be accurately aligned. Researchers have investigated methods for robustly and accurately registering these images for many years. However, recent advancements in imaging systems have made the problem more complex than ever. Currently, the imaging satellites that capture information around the globe have achieved a resolution of less than a meter per pixel. There is an increase in signal complexity for high-resolution SAR images due to how the imaging system operates. Interference between waves gives rise to speckled noise and geometric distortions, making the images very difficult to interpret. This directly affects the image registration accuracy. In this thesis, the complexity of the problem regarding registration between SAR and EO data was described, and methods for registering the images were investigated. The methods were feature- and area-based. The feature-based method used a KAZE filter and SURF descriptor. The method found many key points but few correct correspondences. The area-based methods used FFT and MI, respectively. FFT was deemed best for higher quality images, whereas MI better dealt with the non-linear intensity difference. More complex techniques, such as dense neural networks, were excluded. No method achieved satisfying results on the entire data set, but the area-based methods accomplished complementary results. A conclusion was drawn that the distortions in the SAR images are too significant to register accurately using only CV algorithms. Since the area-based methods achieved good results on images excluding significant distortions, future work should focus on solving the geometrical errors and increasing the registration accuracy

This thesis deals with image registration of MRI images using neural networks. Image registration combines multiple images of the same subject that were taken at different points in time, from different sensors, or from different points of views into a single image and coordinate system. Image registration is widely used in medical imaging and remote sensing. In this thesis feed forward neural networks and wavelet neural networks are used to estimate the parameters of registration. Simulations show that the wavelet networks provide significantly more accurate results than feed forward networks and other proposed methods including genetic algorithms. Both methods are also shown to be robust to noise and changes in parameter ranges.

This thesis is concerned with model-based non-rigid registration of single-modality magnetic resonance images of compressed and uncompressed breast tissue in breast cancer diagnostic/interventional imaging. First, a volumetric registration algorithm is developed which solves the registration as a state estimation problem. Using a static deformation model. To reduce computations, the similarity measure is calculated at some specific points called control points. These control points can be from a low resolution image grid or any irregular image grid. Our numerical analysis has shown that control points placed in the area without much information; i.e with small or no changes in image intensity, yield negligible deformation. Therefore, the selection of the control points can significantly impact the accuracy and computation complexity of the registration algorithms. An extension of the speeded up robust features (SURF) to 3D is proposed for enhanced selection of the control points in deformable image registration. The impact of this new control point selection method on the performance of the registration algorithm is analyzed by comparing it to the case where regular grid control points are used. The results show that the number of control points could be reduced by a factor of ten with new selection methodology without sacrificing performance. Second image registration method is proposed in which, based on a segmented pre-operative image, a deformation model of the breast tissue is developed and discretized in the spatial domain using the method of finite elements. The compression of the preoperative image is modeled by applying smooth forces on the surface of the breast where compression plates are placed. Image registration is accomplished by formulating and solving an optimization problem. The cost function is a similarity measure between the deformed preoperative image and intra-operative image computed at some control point and the decision variables are the tissue interaction forces. / Thesis / Master of Applied Science (MASc)

Image-guided radiation therapy (IGRT) provides accurate and precise tumour targeting. To ensure adequate coverage in IGRT, a planning target volume (PTV) margin is added around the target to account for treatment uncertainties. Treatment plans are designed to deliver a high percentage of the prescription dose to the PTV; thus, portions of healthy tissue are also subjected to high radiation dose. IGRT employs dedicated devices that enable visual assessment of some treatment uncertainties, such as variations in patient set-up. Safe and effective IGRT delivery requires adherence to disease site-specific protocols that describe process details such as imaging technique, alignment method, and corrective action levels. Protocol design is challenging since its effect on treatment accuracy is currently unknown. This thesis aims to understand the interplay between lung IGRT protocol parameters by developing a framework that quantifies geometrical accuracy. Deformable image registration was used to account for changes in target shape and size throughout treatment. Sufficient accuracy was considered when at least 99% of the target surface fell within the PTV. This analysis revealed that the clinical 10 mm PTV margin can be safely reduced by at least 2 mm in each direction. Evaluation of IGRT accuracy was extended to spinal cord alignment. Simulations were carried out with various matching strategies to correct for set-up error, including rotational off-sets. Inappropriate combinations of matching strategies and safety margins resulted in sub-optimal geometrical coverage. Various lung IGRT protocol options were recommended to optimize accuracy and workflow efficiency. For example, an 8 mm PTV margin can be used with spinal cord alignment, a 4 mm cord margin, and up to 5° of rotational error. A more aggressive protocol involved a 6 mm PTV margin with direct target alignment, a 5 mm cord margin, and a 4° rotational tolerance. / Thesis / Master of Science (MSc)

Object Tracking plays a very pivotal role in many computer vision applications such as video surveillance, human gesture recognition and object based video compressions such as MPEG-4. Automatic detection of any moving object and tracking its motion is always an important topic of computer vision and robotic fields. This thesis deals with the problem of detecting the presence of debris or any other unexpected objects in footage obtained during spacecraft launches, and this poses a challenge because of the non-stationary background. When the background is stationary, moving objects can be detected by frame differencing. Therefore there is a need for background stabilization before tracking any moving object in the scene. Here two problems are considered and in both footage from Space shuttle launch is considered with the objective to track any debris falling from the Shuttle. The proposed method registers two consecutive frames using FFT based image registration where the amount of transformation parameters (translation, rotation) is calculated automatically. This information is the next passed to a Kalman filtering stage which produces a mask image that is used to find high intensity areas which are of potential interest.