This thesis examines the perceptions and experiences of pre-registration nursing students with dyslexia in one university in relation to one specific assessment: the Objective Structured Clinical Examination (OSCE). In September 2013, all United Kingdom pre-registration nursing training moved to degree level university programmes. Universities must also ensure that all nursing students meet the fitness to practise criteria laid down by the Nursing and Midwifery Council (NMC, 2015). Current national higher education policy aims to widen participation; this includes those with disabilities. Statistics show that 10% of students attending university in England have a declared disability, the main one being dyslexia. The study university has its own widening participation policy, with 19% of its children’s nursing students currently registered as having dyslexia. The Nursing and Midwifery Council (2010) states that all pre-registration nursing programmes should contain a variety of assessment strategies, to ensure students meet the academic and clinical standards required by the professional nursing and midwifery register. One of the final assessments at the study university, as with many other medical and nursing degrees, is the Objective Structured Clinical Examination, a method of assessment that requires students to perform clinical assessments and answer questions within standardised conditions, within a set time limit. This study aims to explore the ways in which nursing students with dyslexia perceive and experience the OSCE as an assessment method, and to draw conclusions on ways to develop it further. Using a two-phase mixed methods approach, a purposive sample of 24 nursing students in year 3 of their course, was approached to participate in an online questionnaire, with 12 responding. Six students participated further in object elicitation interviews, which were analysed using a ‘Framework’ method. The findings highlight the unique OSCE journeys of study participants, the impact of dyslexia on the individual and the OSCE assessment process. The thesis offers discussion and recommendations around the OSCE as an ‘inclusive’ teaching and assessment method, considering how the design of curricula and assessments assists in recognising students’ individualism and in reducing potential issues. It is the first study to consider the OSCE with regard to such students and offers an opening for future studies focussing on learning difficulties and OSCE assessments within nursing.

Magister Legum - LLM / Stateless children and those at risk of becoming stateless has been an ongoing issue both on a domestic level as well as internationally. In many African countries children face discriminatory and arbitrary nationality laws as a result of which they are not registered and granted citizenship in their country of birth or where they are found or undocumented. Thus, children continue to be stateless and will not be able to register their own children once they become parents. As a result, this creates an issue of transgenerational statelessness which will continue indefinitely and as such, requires attention and action both on a domestic and international level as a matter of urgency. While laws have been enacted in the aim to protect stateless children or children at risk of becoming stateless, the lack of guidelines in the implementation thereof creates a difficulty for children to acquire a nationality. States in this regard have the responsibility to create mechanisms to facilitate the implementation of laws especially when dealing with vulnerable groups such as stateless children.

Résumé : Thèse de DoctoratL’importance de l’imagerie médicale comme élément principal dans plusieurs application médicales et diagnostiques de soin de santé est indispensable. L’intégration des données utiles obtenues des diverses images est vitale pour une analyse appropriée de l’information contenues dans ces images sous observation. Pour que ce processus d’intégration réussisse, une procédure appelée recalage d’image est nécessaire.Le but du recalage d’image consiste à aligner deux images afin de trouver une transformation géométrique qui place une des deux images dans la meilleure correspondance spatiale possible avec l’autre image en optimisant un critère de recalage. Les deux images sont dites image cible et image source. Les méthodes de recalage d’image consistent à avoir référencées par des points de contrôle. Ceci est suivi d’une transformation de recalage qui associe les deux images et d’une fonction définie sur base de la mesure de similarité qui a pour but de mesurer la valeur qualitative de proximité ou encore de degré de concordance entre l’image cible et l’image source. Enfin, un optimisateur qui cherche à trouver la transformation optimale au sein du champ de solution de la recherche, est appliqué.Cette recherche présente un algorithme automatique de recalage d’image dont le but est de résoudre le problème du recalage d’image à multiple modes sur une paire des clichés de tomographie par ordinateur (CT) faite sur les poumons. Dans cette méthode, une étude comparative entre la méthode classique d’optimisation par algorithme du gradient à pas fixe et celle de l’algorithme évolutionniste est menée. L’objectif de cette recherche est d’effectuer l’optimisation par des techniques de recalage d’image ainsi qu’évaluer la performance de ces mêmes techniques afin de doter les spécialistes du domaine médical d’une estimation de combien précis et robuste le processus de recalage serait. Les paires des clichés obtenues de la tomographie par ordinateur faite sur les poumons sont recalées en utilisant l’information mutuelle comme mesure de similarité, la transformation affine ainsi que l’interpolation linéaire. Un optimisateur qui recherche la transformation optimale au sein de l’espace de recherche est appliqué afin de minimiser la fonction économique (fonction objectif).Les études de détermination d’un modèle de transformation qui dépend des paramètres de transformation et de l’identification des mesures de similarité basée sur l’intensité du voxel ont été menées. En alignant la transformation avec les points de control, trois modèles de transformation sont comparés. La transformation affine produit la meilleure reconstitution d’image en comparaison aux transformations non réfléchissantes et projectives. Les résultats de cette recherche sont assez comparables à celles rapportées dans le challenge de recherche EMPIRE 10 et sont conformes à la fois aux principes théoriques aussi bien qu’aux applications pratiques.La contribution de cette recherche réside dans son potentiel à améliorer la compréhension scientifique du recalage d’image des organes anatomiques du corps humain. Cette recherche établie ainsi une base pour une recherche avancée sur l’évaluation de la performance des techniques de recalage et la validation des procédures sur d’autres types d’algorithmes et domaines d’application du recalage d’images comme la détection, la communication par satellite, l’ingénierie biomédicale, la robotique, les systèmes d'information géographique (SIG) et de localisation parmi tant d’autres / D’Tech Thesis SummaryThe importance of medical imaging as a core component of several medical application and healthcare diagnosis cannot be over emphasised. Integration of useful data acquired from different images is vital for proper analysis of information contained in the images under observation. For the integration process to be successful, a procedure referred to as image registration is necessary.The purpose of image registration is to align two images in order to find a geometric transformation that brings one image into the best possible spatial correspondence with another image by optimising a registration criterion. The two images are known as the target image and the source image. Image registration methods consist of having the two images referenced with control points. This is followed by a registration transformation that relates the two images and a similarity metric function that aims to measure the qualitative value of closeness or degree of fitness between the target image and the source image. Finally, an optimiser which seeks an optimal transformation inside the defined solution search space is performed.This research presents an automated image registration algorithm for solving multimodal image registration on lung Computer Tomography (CT) scan pairs, where a comparison between regular step gradient descent optimisation technique and evolutionary optimisation was investigated. The aim of this research is to carry out optimisation and performance evaluation of image registration techniques in order to provide medical specialists with estimation on how accurate and robust the registration process is. Lung CT scan pairs are registered using mutual information as a similarity measure, affine transformation and linear interpolation. In order to minimise the cost function, an optimiser, which seeks the optimal transformation inside the defined search space is applied.Determination of a transformation model that depends on transformation parameters and identification of similarity metric based on voxel intensity were carried out. By fitting transformation to control points, three transformation models were compared. Affine transformation produced the best recovered image when compared to non-reflective similarity and projective transformations. The results of this research compares well with documented results from EMPIRE 10 Challenge research and conforms to both theoretical principles as well as practical applications.The contribution of this research is its potential to increase the scientific understanding of image registration of anatomical body organs. It lays a basis for further research in performance evaluation of registration techniques and validation of procedures to other types of algorithms and image registration application areas, such as remote sensing, satellite communication, biomedical engineering, robotics, geographical information systems and mapping, among others

Image registration plays an important role in pulmonary image analysis. Accurate image registration is a challenging problem when the lungs have deformation with large distance. Registration results estimate the local tissue movement and are useful for studying lung mechanical quantities. In this thesis, we propose a new registration algorithm and a registration scheme to solve lung CT matching problems. Approaches to study lung functions are discussed and presented through a practical application. The overall objective of our project is to develop image registration techniques and analysis approaches to measure lung functions at high resolution. We design a nonrigid volumetric registration algorithm to catch lung motion from a pair of intrasubject CT images acquired at different inflation levels. This registration algorithm preserves both parenchymal tissue volume and vesselness measure, and is regularized by a linear elasticity cost. Validation methods for lung CT matching are introduced and used to evaluate the performance of different registration algorithms. Evaluation shows the feature-based vesselness constraint can efficiently improve the registration accuracy around lung boundaries and in the base lung region. Meanwhile, a new scheme to solve complex registration problem is introduced utilizing both surface and volumetric registration. The first step of this scheme is to register the boundaries of two images using surface registration. The resulting boundary displacements are extended to the entire ROI domains using the Element Free Galerkin Method (EFGM) based on weighted extended B-Splines (WEB-Splines). These displacement fields are used as initial conditions for the tissue volume– and vessel–preserving non-rigid registration over the object domain. Both B-Splines and WEB-Splines are used to parameterize the transformations. Our algorithms achieve high accuracy and provide reasonable lung function maps. The mean errors on landmarks, vessel locations, and fissure planes are on the order of 1 mm (sub-voxel level). Furthermore, we establish methods based on registration derived transformation to analyze mechanical quantities and measure regional lung function. The proposed registration method and lung function measurement are applied on a practical application to detect mechanical alternations in the lung following bronchoalveolar lavage, which achieves satisfactory results and demonstrates the applicability of our proposed approaches.

This thesis mainly focuses on proposing a 4D (three spatial dimensions plus time) tissue-volume preserving non-rigid image registration algorithm for pulmonary 4D computed tomography (4DCT) data sets to provide relevant information for radiation therapy and to estimate pulmonary ventilation. The sum of squared tissue volume difference (SSTVD) similarity cost takes into account the CT intensity changes of spatially corresponding voxels, which is caused by variations of the fraction of tissue within voxels throughout the respiratory cycle. The proposed 4D SSTVD registration scheme considers the entire dynamic 4D data set simultaneously, using both spatial and temporal information. We employed a uniform 4D cubic B-spline parametrization of the transform and a temporally extended linear elasticity regularization of deformation field to ensure temporal smoothness and thus biological plausibility of estimated deformation. A multi-resolution multi-grid registration framework was used with a limited-memory Broyden Fletcher Goldfarb Shanno (LBFGS) optimizer for rapid convergence rate, robustness against local minima and limited memory consumption. The algorithm was prototyped in Matlab and then fully implemented in C++ in Elastix package based on the Insight Segmentation and Registration Toolkit (ITK). We conducted experiments on 2D+t synthetic images to demonstrate the effectiveness of the proposed method. The 4D SSTVD algorithm was also tested on clinical pulmonary 4DCT data sets in comparison with existing 3D pairwise SSTVD algorithm and 4D sum of squared difference (SSD) algorithm. The mean landmark error and mean landmark irregularity were calculated based on manually annotated landmarks on publicly available 4DCT data sets to evaluate the accuracy and temporal smoothness of the registration results. A 4D landmarking software tool was also designed and implemented in Java as an ImageJ plug-in to help facilitate the landmark labeling process in 4DCT data sets.

Current radiation therapy (RT) planning for limiting lung toxicity is based on a uniform lung function with little consideration to the spatial and temporal pattern of lung function. Establishment of relationships between radiation dose and changes in pulmonary function can help predict and reduce the RT-induced pulmonary toxicity. Baseline measurement uncertainty of pulmonary function across scans needs to be assessed, and there is a great interest to compensate the pulmonary function for respiratory effort variations. Respiratory-gated 4DCT imaging and image registration can be used to estimate the regional lung volume change by a transformation-based ventilation metric which is computed directly from the deformation field, or a intensity-based metric which is based on CT density change in the registered image pair. In this thesis, we have evaluated the reproducibility of regional pulmonary function measures using two repeated 4D image acquisitions taken within a short time interval for both transformation-based and intensity-based metrics. Furthermore, we have proposed and compared normalization schemes that correct ventilation images for variations in respiratory effort and assess the reproducibility improvement after effort correction. The major contributions of this thesis include: 1) develop and validate a process for establishing measurement reproducibility in 4DCT-based ventilation, 2) evaluate reproducibility of the transformation-based ventilation measurement, 3) evaluate reproducibility of the intensity-based ventilation measurement, 4) develop and compare different ventilation normalization methods to correct for respiratory effort variation across scans.

Radiation therapy (RT) is commonly used to treat patients with lung cancer. One of the limitations of RT is that irradiation of the surrounding healthy lung tissues during RT may cause damage to the lungs. Radiation-induced pulmonary toxicity may be mitigated by minimizing doses to high-function lung tissues, which we refer to as functional avoidance RT. Lung function can be computed by image registration of treatment planning four-dimensional computed tomography (4DCT), which we refer to as CT ventilation imaging. However, the accuracy of functional avoidance RT is limited by lung function imaging accuracy and artifacts in 4DCT. The goal of this dissertation is to improve the accuracy of functional avoidance RT by overcoming those two limitations. A common method for estimating lung ventilation uses image registration to align the peak exhale and inhale 3DCT images. This approach called the 2-phase local expansion ratio is limited because it assumes no out-of-phase lung ventilation and may underestimate local lung ventilation. Out-of-phase ventilation occurs when regions of the lung reach their maximum (minimum) local volume in a phase other than the peak of inhalation (end of exhalation). This dissertation presents a new method called the N-phase local expansion ratio for detecting and characterizing locations of the lung that experience out-of-phase ventilation. The N-phase LER measure uses all 4DCT phases instead of two peak phases to estimate lung ventilation. Results show that out-of-phase breathing was common in the lungs and that the spatial distribution of out-of-phase ventilation varied from subject to subject. On average, 49% of the out-of-phase regions were mislabeled as low-function by the 2-phase LER. 4DCT and Xenon-enhanced CT (Xe-CT) of four sheep were used to evaluate the accuracy of 2-phase LER and N-phase LER. Results show that the N-phase LER measure was more correlated with the Xe-CT than the 2-phase LER measure. These results suggest that it may be better to use all 4DCT phases instead of the two peak phases to estimate lung function. The accuracy of functional avoidance RT may also be improved by reducing the impact of artifacts in 4DCT. In this dissertation, we propose a a geodesic density regression (GDR) algorithm to correct artifacts in one breathing phase by using artifact-free data in corresponding regions of the other breathing phases. Local tissue density change associated with CT intensity change during respiration is accommodated in the GDR algorithm. Binary artifact masks are used to exclude regions of artifacts from the regression, i.e., the GDR algorithm only uses artifact-free data. The GDR algorithm estimates an artifact-free CT template image and its time flow through a respiratory cycle. Evaluation of the GDR algorithm was performed using both 2D CT time-series images with simulated known motion artifacts and treatment planning 4DCT with real motion artifacts. The 2D results show that there is no significant difference (p-value = 0.95) between GDR regression of artifact data using artifact masks and regression of artifact-free data. In contrast, significant errors (p-value = 0.005) were present in the estimated Jacobian images when artifact masks were not used. We also demonstrated the effectiveness of the GDR algorithm for removing real duplication, misalignment, and interpolation artifacts in 4DCT. Overall this dissertation proposes methods that have the potential to improve functional avoidance RT by accommodating out-of-phase ventilation, and removing motion artifacts in 4DCT using geodesic image regression.

Digital medical atlases can contain anatomical information which is valuable for medical doctors in diagnosing and treating illnesses. The increased availability of such atlases has created an interest for computer algorithms which are capable of integrating such atlas information into patient specific dataprocessing. The field of medical image registration aim at calculating how to match one medical image to another. Here the atlas information could give important hints of which kinds of motion are plausible in different locations of the anatomy. Being able to incorporate such atlas specific information could potentially improve the matching of images and plausibility of image registration - ultimately providing a more correct information on which to base health care diagnosis and treatment decisions. In this licentiate thesis a generic signal processing framework is derived : Global Linear Optimization (GLO). The power of the GLO framework is first demonstrated quantitatively in a very high performing image denoiser. Important proofs of concepts are then made deriving and implementing three important capabilities regarding adaptive filtering of vector fields in medica limage registration: Global regularization with local anisotropic certainty metric. Allowing sliding motion along organ and tissue boundaries. Enforcing an incompressible motion in specific areas or volumes. In the three publications included in this thesis, the GLO framework is shown to be able to incorporate one each of these capabilities. In the third and final paper a demonstration is made how to integrate more and more of the capabilities above into the same GLO to perform adaptive processing on relevant clinical data. It is shown how each added capability improves the result of the image registration. In the end of the thesis there is a discussion which highlights the advantage of the contributions made as compared to previous methods in the scientific literature. / Dynamic Context Atlases for Image Denoising and Patient Safety

This thesis compares and contrasts currents- and varifolds-based diffeomorphic image registration approaches for registering tree-like structures in the lung and surface of the lung. In these approaches, curve-like structures in the lung—for example, the skeletons of vessels and airways segmentation—and surface of the lung are represented by currents or varifolds in the dual space of a Reproducing Kernel Hilbert Space (RKHS). Currents and varifolds representations are discretized and are parameterized via of a collection of momenta. A momenta corresponds to a line segment via the coordinates of the center of the line segment and the tangent direction of the line segment at the center. A momentum corresponds to a mesh via the coordinates of the center of the mesh and the normal direction of the mesh at the center. The magnitude of the tangent vector for the line segment and the normal vector for the mesh are the length of the line segment and the area of the mesh respectively. A varifolds-based registration approach is similar to currents except that two varifolds representations are aligned independent of the tangent (normal) vector orientation. An advantage of varifolds over currents is that the orientation of the tangent vectors can be difficult to determine especially when the vessel and airway trees are not connected. In this thesis, we examine the image registration sensitivity and accuracy of currents- and varifolds-based registration as a function of the number and location of momenta used to represent tree like-structures in the lung and the surface of the lung. The registrations presented in this thesis were generated using the Deformetrica software package, which is publicly available at www.deformetrica.org.

Lung disease affects tens of millions of Americans, making it one of the most common medical conditions in the United States. Many of these lung diseases are classified as chronic airway disease. Because of this, it is important to be able to catch the development early so as to begin treatment as soon as possible to delay the progression and subsequently monitor that progression. One method of doing so is the use of quantitative computed tomography (CT). Study of the airway anatomy can be quantified using such measures as minor inner diameter (MinD), major inner diameter (MajD), wall thickness (WT), inner area (IA), and outer area (OA). Changes in these measures can then be tracked over time to determine how the airways are being affected by disease. The challenge with the desired longitudinal imaging is that prolonged radiation exposure can be dangerous to the patient. In order to make longitudinal imaging more feasible, it is important to determine what quantitative measures can reliably be made at different radiations doses so as to optimize radiation dose and quantitative assessment. Working to make this determination, three different radiation doses were tested to evaluate their quantitative outputs. A high dose (14.98 mGy), medium dose (6.00), and low dose (0.74 mGy) were used to image six different porcine subjects. Images were collected at these doses both while the lungs were in-vivo and once the lungs had been fixed and excised ex-vivo. All of the scans were then processed using APOLLO (VIDA Diagnostics). From the complete airway trees, quantitative measures were collected from thirty-five airways. For the whole lung analysis, the medium and low dose in-vivo scans and the high dose ex-vivo scans were compared to the high dose in-vivo scans to compare assess MinD, MajD, WT, IA, and OA. Then, in order to determine how well the CT measures represent the actual anatomy, a total of thirteen cube samples containing airways were segmented from one of the lungs (based on volume analysis of the lung pre- and post-fixation and visual inspection). The cubes were imaged in CT, for the purpose of aiding in the establishment of original location and studying the effect of a narrowed imaging window, and microscopic CT (μCT). Since μCT can have a resolution on the scale of microns, the values measured in these images were considered ground-truth. The CT and μCT cubes were then registered to the high dose ex-vivo scan so as to compare the cube values with the ex-vivo values from each of the three doses. The same five measures were collected and analyzed. The MinD, MajD, WT, IA, OA were statistically analyzed between the three in-vivo radiation dose scan sets, the high dose in- and ex-vivo scans, and the µCT cube, CT cube, and the three ex-vivo radiation dose sets. Preliminary results for the in-vivo scans show that the low dose and medium dose scans can reliably (< 5% error) be used to evaluate airways with minor diameters between 3.42 mm and 10.34 mm and major diameters between 3.98 mm and 12.06 mm. Comparison of the high-dose in-vivo and ex-vivo scans showed that the fixation and excision of the lungs did not significantly affect the ex-vivo lungs' ability to be used as a model for the in-vivo lungs. Finally, analysis of the various forms of the ex-vivo airways showed that there were few statistically significant differences between the datasets. These results support the use of using the low (0.74 mGy) radiation dose when studying airway disease affecting airways with minor diameters between 3.42 mm and 10.34 mm and major diameters between 3.98 mm and 12.06 mm. They also show that the quantitative measures from CT are representative of the true measures of the airways.