<p>The typical High Dose Rate (HDR) lung brachytherapy procedure involves inserting treatment catheters into the bronchi next to the tumour location using a bronchoscope. The anterior-posterior and lateral fluoroscopy images are acquired in order to localize the catheters prior to treatment. Although, these images enable accurate reconstruction of the catheter location, they do not allow for the visualization of the tumour or organs-at-risk due to poor soft tissue contrast. Although CT images offer an improved soft tissue contrast, moving the patient with catheters in place prior to each treatment is impractical.</p> <p>An alternative option is to use prior diagnostic or external beam radiation treatment planning CT images. These images cannot be used for treatment planning directly because of the variation in patient positioning between the CT and orthogonal images acquisition. In order to account for positioning differences, a 2D/3D registration algorithm that registers the orthogonal images with a previously acquired CT data was developed. The algorithm utilizes a rigid registration model based on a pixel/voxel intensity matching approach. A similarity measure combining normalized mutual information (NMI), image gradient, and intensity difference was developed. Evaluation of the algorithm was performed using tissue equivalent phantoms, and, in the clinical setting using data from six patients. The mean registration error was 2.1 mm and 3.2 mm for phantoms and patients respectively.</p> <p>External objects such as the treatment table and ECG leads are often in CT images, affecting the above mentioned 2D/3D registration. To address this, an algorithm for automatic removal of external objects from CT images was developed. This was applied to automatic contouring and removal of the fiducial markers in CT images used for external beam radiation therapy treatment planning for breast cancer. The algorithm was further modified to compute the girth of patients as part of a diagnostic radiology clinical trial.</p> / Doctor of Philosophy (PhD)

This thesis outlines the development of an automatic image registration algorithm for matching 3D CT data to 2D fluoroscope X-ray images. The registration is required in order to calculate a transformation for measurements in the 2D image into the 3D representation. The algorithm achieves the registration by generating digitally reconstructed radiographs from the CT data set. The radiographs are 2D projection images, and therefore may be compared with the 2D Fluoroscope images. The X-ray and fluoroscope images were compared using the photometric-based registration algorithm, pseudocorrelation, with X^2 as the distance metric. An automated search algorithm was implemented using the Downhill Simplex of Nelder and Meade. The algorithm was successful in locating the position and orientation of the CT data set for calculating a digitally reconstructed radiograph to match the fluoroscope image. The CT data set was located with a maximum mean position error of 2.4 mm in xy, 4.4 mm in z, and xyz axial rotation within 0.5°. The standard deviation given 1800 random starting locations was 9.3 mm in x, 12.7 mm in y, 16.9 mm in z, xz axial rotation 2.5°, and y axial rotation of 1.9°. The search algorithm was successful in handling gross misalignment, however there were difficulties in convergence once within the vicinity of the global minimum. It is suggested to implement a hybrid search technique, switching to a conjugate gradient search algorithm once in the vicinity of the global minimum. An additional refinement would be a possible change of the distant metric, or the registration algorithm, once within the vicinity of the global minimum. Additional investigation needs to be directed towards testing the algorithm with live fluoroscope and CT data. This is required in order to assess registration performance when comparing different imaging modalities. / Thesis / Master of Engineering (ME)

Nearly 350 years of politically sanctioned domination over Blacks ended with the passage of the Voting Rights Act (VRA) in 1965. The federal regulation of voter and election law sought to end retrogressions in representation by intentional or effectual laws. In the VRA's wake, race based politics and policy rooted in White supremacy were curtailed with the gradual representation of communities of color in all levels of government. Shelby County v Holder (2013) obstructed progress by effectively terminating preclearance of legal changes by the federal government. Since Shelby, retrogression of voter registration is once again on the rise. Remedies for retrogression require litigation and matriculation through the courts. This process is time consuming and allows states to conduct election law with minimal interruption until decisions are rendered. Research predating the passage of the Voting Rights Act by Matthews and Prothro indicated that there was a significant correlation between growing minority populations and the severity of election and voter laws. This paper seeks to determine if growing minority populations, in part due to disproportionately large in-migration, correlates with declining voter registration rates. These voter registration rates are due to substantive legal changes and procedural enforcement changes. Retrogression in Black, White, and Latinx is shown in analyzing state voter registration data. Using a time-series multivariate analysis to compare impact on Black, Latinx, and White communities across counties in North Carolina and Alabama, this paper determines that growing minority populations and in-migration do not have consistent statistical significance in explaining declining voter registration rates for Blacks and Latinx communities based on data from the US Census Bureau's American Community Survey and the Alabama and North Carolina Board of Elections. Periodic retrogression in voter registration for the Black community show statistically significant positive associations with increasing population sizes. The Black community experiences retrogression via statistically significant negative associations in national election years, and Black voter registration rates recover in off-year elections. Data indicates that there may be a decrease in representation of larger minority communities that Black communities are able to overcome. There is a strong association between decreasing voter registration rates and larger Latinx communities while the opposite is true of Black communities. The Latinx community voter registration have statistically significant negative associations with increasing population sizes and during national election years, with recovering registration rates in off-year elections. / Master of Arts / Nearly 350 years of politically sanctioned domination over Blacks ended with the passage of the Voting Rights Act (VRA) in 1965. The federal regulation of voter and election law sought to end retrogressions in representation by intentional or effectual laws. In the VRA's wake, race based politics and policy rooted in White supremacy were curtailed with the gradual representation of communities of color in all levels of government. Shelby County v Holder (2013) obstructed progress by effectively terminating preclearance of legal changes by the federal government. Since Shelby, retrogression of voter registration is once again on the rise. Remedies for retrogression require litigation and matriculation through the courts. This process is time consuming and allows states to conduct election law with minimal interruption until decisions are rendered. Research predating the passage of the Voting Rights Act by Matthews and Prothro indicated that there was a significant correlation between growing minority populations and the severity of election and voter laws. This paper seeks to determine if growing minority populations, in part due to disproportionately large in-migration, correlates with declining voter registration rates. These voter registration rates are due to substantive legal changes and procedural enforcement changes. Retrogression in Black, White, and Latinx is shown in analyzing state voter registration data. Findings determine that for Black, Latinx, and White communities across counties in North Carolina and Alabama, growing minority populations and in-migration do not have significance in explaining declining voter registration rates for Blacks based on data from the US Census Bureau's American Community Survey and the Alabama and North Carolina Board of Elections. However, voter registration rates decrease as Latinx communities increase in size while the opposite is true of Black communities. Retrogression in Black and Latinx voter registration during national election years and rebound in off-year elections.

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This quantitative study utilized secondary data from one large four-year, state university in the southwestern US. The relationship between registration time and academic performance was examined as well as the relationship between registration time and retention of first-time-in-college (FTIC) undergraduate students during their first semester of enrollment at the university. The differences between decided and undecided students were tested regarding students’ academic performance and retention of the same population. The study population for the fall 2011 semester included 6,739 freshmen, and the study population for the fall 2012 semester included 4,454 freshmen. Through multiple and logistic regression models, registration time was shown to statistically have a relationship with academic performance and retention (p < .05). Later registrants showed to have a negative relationship with GPA and were less likely to return the following spring semester. The explained variance (R2) for both measures of academic performance and retention along with descriptive statistics are also presented. A Mann Whitney U test and chi square test indicated that a statistically significant association between decided and undecided students exists for academic performance and retention (p < .05). Decided major students performed better as measured by semester GPA performance and were more likely to return the following spring semester. Recommendations and implications are issued regarding future research, policy, and practice.

Different medical imaging modalities provide complementary anatomical and functional information. One increasingly important use of such information is in the clinical management of cardiovascular disease. Multi-modality data is helping improve diagnosis accuracy, and individualize treatment. The Clinical Research Imaging Centre at the University of Edinburgh, has been involved in a number of cardiovascular clinical trials using longitudinal computed tomography (CT) and multi-parametric magnetic resonance (MR) imaging. The critical image processing technique that combines the information from all these different datasets is known as image registration, which is the topic of this thesis. Image registration, especially multi-modality and multi-parametric registration, remains a challenging field in medical image analysis. The new registration methods described in this work were all developed in response to genuine challenges in on-going clinical studies. These methods have been evaluated using data from these studies. In order to gain an insight into the building blocks of image registration methods, the thesis begins with a comprehensive literature review of state-of-the-art algorithms. This is followed by a description of the first registration method I developed to help track inflammation in aortic abdominal aneurysms. It registers multi-modality and multi-parametric images, with new contrast agents. The registration framework uses a semi-automatically generated region of interest around the aorta. The aorta is aligned based on a combination of the centres of the regions of interest and intensity matching. The method achieved sub-voxel accuracy. The second clinical study involved cardiac data. The first framework failed to register many of these datasets, because the cardiac data suffers from a common artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I developed a new preprocessing technique that is able to correct the artefacts in the functional data using data from the anatomical scans. The registration framework, with this preprocessing step and new particle swarm optimizer, achieved significantly improved registration results on the cardiac data, and was validated quantitatively using neuro images from a clinical study of neonates. Although on average the new framework achieved accurate results, when processing data corrupted by severe artefacts and noise, premature convergence of the optimizer is still a common problem. To overcome this, I invented a new optimization method, that achieves more robust convergence by encoding prior knowledge of registration. The registration results from this new registration-oriented optimizer are more accurate than other general-purpose particle swarm optimization methods commonly applied to registration problems. In summary, this thesis describes a series of novel developments to an image registration framework, aimed to improve accuracy, robustness and speed. The resulting registration framework was applied to, and validated by, different types of images taken from several ongoing clinical trials. In the future, this framework could be extended to include more diverse transformation models, aided by new machine learning techniques. It may also be applied to the registration of other types and modalities of imaging data.

In this thesis a methodology for representing 3D subjects and their deformations in adverse situations is studied. The study is focused in providing methods based on registration techniques to improve the data in situations where the sensor is working in the limit of its sensitivity. In order to do this, it is proposed two methods to overcome the problems which can difficult the process in these conditions. First a rigid registration based on model registration is presented, where the model of 3D planar markers is used. This model is estimated using a proposed method which improves its quality by taking into account prior knowledge of the marker. To study the deformations, it is proposed a framework to combine multiple spaces in a non-rigid registration technique. This proposal improves the quality of the alignment with a more robust matching process that makes use of all available input data. Moreover, this framework allows the registration of multiple spaces simultaneously providing a more general technique. Concretely, it is instantiated using colour and location in the matching process for 3D location registration.

Deformable image registration (DIR) is a type of registration that calculates a deformable vector field (DVF) between two image data sets and permits contour and dose propagation. However the calculation of a DVF is considered an ill-posed problem, as there is no exact solution to a deformation problem, therefore all DVFs calculated contain errors. As a result it is important to evaluate and assess the accuracy and limitations of any DIR algorithm intended for clinical use. The influence of image quality on the DIR algorithms performance was also evaluated. The hybrid DIR algorithm in RayStation 4.0.1.4 was assessed using a number of evaluation methods and data. The evaluation methods were point of interest (POI) propagation, contour propagation and dose measurements. The data types used were phantom and patient data. A number of metrics were used for quantitative analysis and visual inspection was used for qualitative analysis. The quantitative and qualitative results indicated that all DVFs calculated by the DIR algorithm contained errors which translated into errors in the propagated contours and propagated dose. The results showed that the errors were largest for small contour volumes (<20cm3) and for large anatomical volume changes between the image sets, which pushes the algorithms ability to deform, a significant decrease in accuracy was observed for anatomical volume changes of greater than 10%. When the propagated contours in the head and neck were used for planning the errors in the DVF were found to cause under dosing to the target tumour by up to 32% and over dosing to the organs at risk (OAR) by up to 12% which is clinically significant. The results also indicated that the image quality does not have a significant effect on the DIR algorithms calculations. Dose measurements indicated errors in the DVF calculations that could potentially be clinically significant. The results indicate that contour propagation and dose propagation must be used with caution if clinical use is intended. For clinical use contour propagation requires evaluation of every propagated contour by an expert user and dose propagation requires thorough evaluation of the DVF.

Registration of a group of images generally only gives a pointwise, dense correspondence defined over the whole image plane or volume, without having any specific description of any common structure that exists in every image. Furthermore, identifying tissue classes and structures that are significant across the group is often required for analysis, as well as the correspondence. The overall aim is instead to perform registration, segmentation, and modelling simultaneously, so that the registration can assist the segmentation, and vice versa. However, structural information does play a role in conventional registration, in that if the registration is successful, it would be expected structures to be aligned to some extent. Hence, we perform initial experiments to investigate whether there is explicit structural information present in the shape of the registration objective function about the optimum. We perturbed one image locally with a diffeomorphism, and found interesting structure in the shape of the quality of fit function. Then, we proceed to add explicit structural information into registration framework, using various types of structural information derived from the original intensity images. For the case of MR brain images, we augment each intensity image with its own set of tissue fraction images, plus intensity gradient images, which form an image ensemble for each example. Then, we perform groupwise registration by using these ensembles of images. We apply the method to four different real-world datasets, for which ground-truth annotation is available. It is shown that the method can give a greater than 25% improvement on the three difficult datasets, when compared to using intensity-based registration alone. On the easier dataset, it improves upon intensity-based registration, and achieves results comparable with the previous method.

The main goal of this thesis is based on the term of introduction of electronic sales records in the Slovak Republic to highlight the successes but also the shortcomings of this system. The theoretical part is to determine the issue of tax evasion, as one of the main reasons why there is very compulsory electronic records. The next chapter analyses the possible ways of dealing with electronic sales records and compares it with the method chosen in the Czech Republic. The practical part verifies the system's benefits for businesses questionnaire method. While estimates of the contribution for the state based on an analysis of sales, profits and taxes paid of selected businesses. The last part is made up of a model to help business in Slovakia to decide between the use of electronic records sales method for using an electronic cash register, or virtual cash register.