Cardiac magnetic resonance (CMR) imaging is the gold standard imaging technique for assessment of ventricular dimensions and function. CMR also allows assessment of ventricular deformation but this requires additional imaging sequences and time consuming post processing which has limited its widespread use. A novel CMR analysis software package, ‘feature tracking’ (Tom Tec, Germany) can measure ventricular deformation directly from cine CMR images. This thesis seeks to further our understanding of the feasibility of feature tracking to assess myocardial deformation and volumetric measures. Chapter 3 validates normal ranges for deformation parameters and compares values against traditional tagging measures. The work identifies global circumferential strain measures as being the most reproducible. In chapters 4 and 5, feature tracking values for left and right ventricular strain are compared with echocardiography derived speckle tracking indices of deformation. For left ventricular (LV) parameters, circumferential and longitudinal strain are most consistent and for the right ventricular (RV) measures, assessment of free wall strain using feature tracking shows promise and with modifications in algorithms is likely to further improve in the future. Chapter 6 assesses the ability of feature tracking to measure diastolic function. The results show that radial diastolic velocities and longitudinal diastolic strain rates can predict diastolic dysfunction (as diagnosed by echocardiography) with acceptable levels of sensitivity and specificity, particularly when used in combination. 11 The use of feature tracking to provide automated measures of ventricular volumes, mass and ejection fraction is assessed in chapter 7. Feature tracking in this context shows acceptable correlation but poor absolute agreement with manual contouring and further adjustments to algorithms is necessary to improve its accuracy. This work offers insights into the use of feature tracking for the assessment of ventricular deformation parameters. It is a technique with advantages over CMR tagging methods and given the speed of post processing has the potential to become the CMR preferred assessment for strain quantification in the future.

Current situation: Educated and qualified nurse can fully do her job, give a patient bio-psycho-social-spiritual needs and satisfy them. This can happen only if optimal conditions are given to her, which means the optimal number of nursing staff. The aim of the study was to set the optimal number of nursing staff at surgical department in the Hospital of the Vysočina region, which was chosen. A standardized methodology of Pochylá and Pochylý (1999 and 2008) was used. Methodology and the research complex: Quantitative-qualitative method was used in the research. Detailed interviews were used in the first part of the study while talking to nurses of the surgical department of the Hospital of the Vysočina region. In phase two, 26 special nursing procedures were selected and divided into four thematic parts. 520 measurements were made in total, average time of making each special nursing procedure was set. In phase three, average values of nursing one patient in 12 hours at surgical department were set. Daily shots of the procedures were being taken for 14 days (Monday-Sunday). Optimal numbers of nursing staff and their qualified substitution in the Hospital of the Vysočina region were set in the last stage of the research. Results: The research question and three hypotheses were answered by performing and processing the research. The result is that there are statistically major differences in average value of basic and special nursing. 21 patients in average were treated in the hospital in the first week of research. The average nursing time for this number of patients in 12 hours was 3585 minutes 1027 minutes of basic nursing care and 1718 minutes of special nursing procedures. In the second week of research, 24 patients were treated at the department in average. The average nursing time for this number of patients was 3647 minutes in 12 hours 1036 minutes of basic nursing care and 1675 minutes of special nursing procedures. Statistically major differences were confirmed even during special nursing procedures. The longest average time was measured while incoming of patients, transferring, discharging (14,31 minutes) and the shortest average time was measure while applicating medicine into body cavities (0,5 minutes). Based on the research we can say that the number of staff at selected department is for a 12-hour shift optimal. Recommended number of full-time employees is 5, the real number is 5,4. Conclusion and recommendation: The output of this thesis is recommendation for management of nursing care regarding the issue of getting and maintaining the optimal number of nursing staff.

Les maladies cardiovasculaires sont parmi les principales causes de mortalité à l’échelle mondiale. Un certain nombre de maladies cardiaques peuvent être identifiées et localisées par l’analyse du mouvement et de la déformation cardiaques à partir de l’imagerie médicale. Cependant, l’utilisation de ces techniques en routine clinique est freinée par le manque d’outils de quantification efficaces et fiables. Dans cette thèse, nous introduisons un algorithme de quantification appliqué aux images IRM marquées. Nous présentons ensuite un pipeline de simulation qui génère des séquences cardiaques synthétiques (US et IRM). Les principales contributions sont décrites ci-dessous. Tout d’abord, nous avons proposé une nouvelle extension 3D de la méthode de la phase harmonique. Le suivi de flux optique en utilisant la phase a été combiné avec un modèle de régularisation anatomique afin d’estimer les mouvements cardiaques à partir des images IRM marquées. En particulier, des efforts ont été faits pour assurer une estimation précise de la déformation radiale en imposant l’incompressibilité du myocarde. L’algorithme (dénommé HarpAR) a ensuite été évalué sur des volontaires sains et des patients ayant différents niveaux d’ischémie. HarpAR a obtenu la précision de suivi comparable à quatre autres algorithmes de l’état de l’art. Sur les données cliniques, la dispersion des déformations est corrélée avec le degré de fibroses. De plus, les segments ischémiques sont distingués des segments sains en analysant les courbes de déformation. Deuxièmement, nous avons proposé un nouveau pipeline de simulation pour générer des séquences synthétiques US et IRM pour le même patient virtuel. Les séquences réelles, un modèle électromécanique (E/M) et les simulateurs physiques sont combinés dans un cadre unifié pour générer des images synthétiques. Au total, nous avons simulé 18 patients virtuels, chacun avec des séquences synthétiques IRM cine, IRM marquée et US 3D. Les images synthétiques ont été évaluées qualitativement et quantitativement. Elles ont des textures d’images réalistes qui sont similaires aux acquisitions réelles. De plus, nous avons également évalué les propriétés mécaniques des simulations. Les valeurs de la fraction d’éjection et de la déformation locale sont cohérentes avec les valeurs de référence publiées dans la littérature. Enfin, nous avons montré une étude préliminaire de benchmarking en utilisant les images synthétiques. L'algorithme générique gHarpAR a été comparé avec un autre algorithme générique SparseDemons en termes de précision sur le mouvement et la déformation. Les résultats montrent que SparseDemons surclasse gHarpAR en IRM cine et US. En IRM marquée, les deux méthodes ont obtenu des précisions similaires sur le mouvement et deux composants de déformations (circonférentielle et longitudinale). Toutefois, gHarpAR estime la déformation radiale de manière plus précise, grâce à la contrainte d’incompressibilité du myocarde. / Cardiovascular disease is one of the major causes of death worldwide. A number of heart diseases can be diagnosed through the analysis of cardiac images after quantifying shape and function. However, the application of these deformation quantification algorithms in clinical routine is somewhat held back by the lack of a solid validation. In this thesis, we mainly introduce a fast 3D tagged MR quantification algorithm, as well as a novel pipeline for generating synthetic cardiac US and MR image sequences for validation purposes. The main contributions are described below. First, we proposed a novel 3D extension of the well-known harmonic phase tracking method. The point-wise phase-based optical flow tracking was combined with an anatomical regularization model in order to estimate anatomically coherent myocardial motions. In particular, special efforts were made to ensure a reasonable radial strain estimation by enforcing myocardial incompressibility through the divergence theorem. The proposed HarpAR algorithm was evaluated on both healthy volunteers and patients having different levels of ischemia. On volunteer data, the tracking accuracy was found to be as accurate as the best candidates of a recent benchmark. On patient data, strain dispersion was shown to correlate with the extent of transmural fibrosis. Besides, the ischemic segments were distinguished from healthy ones from the strain curves. Second, we proposed a simulation pipeline for generating realistic synthetic cardiac US, cine and tagged MR sequences from the same virtual subject. Template sequences, a state-of-the-art electro-mechanical (E/M) model and physical simulators were combined in a unified framework for generating image data. In total, we simulated 18 virtual patients (3 healthy, 3 dyssynchrony and 12 ischemia), each with synthetic sequences of 3D cine MR, US and tagged MR. The synthetic images were assessed both qualitatively and quantitatively. They showed realistic image textures similar to real acquisitions. Besides, both the ejection fraction and regional strain values are in agreement with reference values published in the literature. Finally, we showed a preliminary benchmarking study using the synthetic database. We performed a comparison between gHarpAR and another tracking algorithm SparseDemons using the virtual patients. The results showed that SparseDemons outperformed gHarpAR in processing cine MR and US images. Regarding tagged MR, both methods obtained similar accuracies on motion and two strain components (circumferential and longitudinal). However, gHarpAR quantified radial strains more accurately, thanks to the myocardial incompressibility constraint. We conclude that motion quantification solutions can be improved by designing them according to the image characteristics of the modality and that a solid evaluation framework can be a key asset in comparing different algorithmic options.