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Antal datortomografiundersökningar har ökat under flera år i Sverige tack vare tekniska utvecklingar och ökad tillgänglighet på sjukvård. Södertälje sjukhus röntgenavdelningen är i behov av att byta ut en utdaterad datortomograf (eng: Computed tomography, CT) och avdelningen för medicinsk teknik har föreslagit ett inköp av en CT med fotonräknande-detektor. Bilddata framställdes genom en fantomstudie för att påvisa förhållandet mellanstråldosparametern CTDIvol och kontrast-brus-förhållandet (CNR) hos CT-systemen: SOMATOM Drive och NAEOTOM Alpha. Den genererade datan påvisade att det finns en väsentlig skillnad i CNR-CTDIvol-förhållandet mellan SOMATOM Drive och NAEOTOM Alpha. Resultaten tydliggör att NAEOTOM Alpha kan producera bilder med betydligt mindre brus vid lägre stråldoser. Ett inköp av en fotonräknande detektor CT skulle kunna rättfärdigas utifrån bildkvalitéförbättringen som systemet kan erbjuda. / The number of computed tomography (CT) scans has increased during the past years in Sweden due to technical advancements and increased availability of healthcare. The x-ray department at Södertälje hospital is in need of replacing an outdated computed tomography and the departmentof clinical engineering has proposed a purchase of a photon-counting detector CT. Image data was produced through a phantom study to demonstrate the relationship between the parameter CTDIvol radiation dose and the contrast-to-noise ratio (CNR) of the CT systems: SOMATOM Drive and NAEOTOM Alpha. The generated data demonstrated that there is a substantial difference in the CNR-CTDIvol relationship between SOMATOM Drive and NAEOTOM Alpha. The results entail that NAEOTOM Alpha can produce images with considerably less noise at lower radiation doses. The purchase of a photon-counting CT could be justified by the improved image quality it can offer.

Yes / This review examines the factors that can influence an investigation into the unexpected death of a child before considering if using imaging techniques could be of benefit. Method A systematic search strategy was adopted to search databases using keywords, these results were then subjected to inclusion and exclusion criteria to filter and refine the evidence base further. Discussion More research is published on the use of MRI in comparison with other modalities. There is evidence in the case of MRI in particular that its use could be of benefit in identifying and ruling out potential causes of death in children. Conclusion More research is needed on the use of CT but the routine use of MRI in child death investigation could now be considered. Ethical considerations appear to be a barrier to research in this area and discussions as to how such considerations can be overcome is necessary.

Computed Tomography (CT) imaging provides invaluable insight into internal structures of objects and organisms, which is critical for applications ranging from materials science to medical diagnostics. In CT data, an object is represented by a 3D reconstruction that is generated by combining multiple 2D X-ray images taken from various angles around the object. Each voxel, a volumetric pixel, within the reconstructed volume represents a small cubic element, allowing for detailed spatial representation. To extract meaningful information from CT imaging data and facilitate analysis and interpretation, accurate segmentation of internal structures is essential. However, this can be challenging due to various artifacts introduced by the physics of a CT scan and the properties of the object being imaged. This dissertation directly addresses this challenge by using deep learning techniques. Specifically, Convolutional Neural Networks (CNNs) are used for segmentation. However, they face the problem of limited training data. Data scarcity is addressed by data augmentation through the unsupervised generation of synthetic training data and the use of 2D and 3D data augmentation methods. A combination of these augmentation strategies allows for streamlining segmentation in voxel data and effectively addresses data scarcity. Essentially, the work aims to simplify training of CNNs, using minimal or no labeled data. To enhance accessibility to the results of this thesis, two user-friendly software solutions, unpAIred and AiSeg, have been developed. These platforms enable the generation of training data, data augmentation, as well as training, analysis, and application of CNNs. This cumulative work first examines simpler but efficient conventional data augmentation methods, such as radiometric and geometric image manipulations, which are already widely used in literature. However, these methods are usually randomly applied and do not follow a specific order. The primary focus of the first paper is to investigate this approach and to develop both online and offline data augmentation pipelines that allow for systematic sequencing of these operations. Offline augmentation involves augmenting training data stored on a drive, while online augmentation is performed dynamically at runtime, just before images are fed to the CNN. It is successfully shown that random data augmentation methods are inferior to the new pipelines. A careful comparison of 3D CNNs is then performed to identify optimal models for specific segmentation tasks, such as carbon and pore segmentation in CT scans of Carbon Reinforced Concrete (CRC). Through an evaluation of eight 3D CNN models on six datasets, tailored recommendations are provided for selecting the most effective model based on dataset characteristics. The analysis highlights the consistent performance of the 3D U-Net, one of the CNNs, and its residual variant, which excel at roving (a bundle of carbon fibers) and pore segmentation tasks. Based on the augmentation pipelines and the results of the 3D CNN comparison, the pipelines are extended to 3D, specifically targeting the segmentation of carbon in CT scans of CRC. A comparative analysis of different 3D augmentation strategies, including both offline and online augmentation variants, provides insight into their effectiveness. While offline augmentation results in fewer artifacts, it can only segment rovings already present in the training data, while online augmentation is essential for effectively segmenting different types of rovings contained in CT scans. However, constraints such as limited diversity of the dataset and overly aggressive augmentation that resulted in segmentation artifacts require further investigation to address data scarcity. Recognizing the need for a larger and more diverse dataset, this thesis extends the results of the three former papers by introducing a deep learning-based augmentation using a Generative Adversarial Network (GAN), called Contrastive Unpaired Translation (CUT), for synthetic training data generation. By combining the GAN with augmentation pipelines, semi-supervised and unsupervised end-to-end training methods are introduced and the successful generation of training data for 2D pore segmentation is demonstrated. However, challenges remain in achieving a stable 3D CUT implementation, which warrants further research and development efforts. In summary, the results of this dissertation address the challenges of accurate CT data segmentation in materials science through deep learning techniques and novel 2D and 3D online and offline augmentation pipelines. By evaluating different 3D CNN models, tailored recommendations for specific segmentation tasks are provided. Furthermore, the exploration of deep learning-based augmentation using CUT shows promising results in the generating synthetic training data. Future work will include the development of a stable implementation of a 3D CUT version, the exploration of new model architectures, and the development of sub-voxel accurate segmentation techniques. These have the potential for significant advances in segmentation in tomography data.:Abstract IV Zusammenfassung VI 1 Introduction 1 1.1 Thesis Structure 2 1.2 Scientific Context 3 1.2.1 Developments in the Segmentation in Tomography Data 3 1.2.2 3D Semantic Segmentation using Machine Learning 5 1.2.3 Data Augmentation 6 2 Developed Software Solutions: AiSeg and unpAIred 9 2.1 Software Design 10 2.2 Installation 11 2.3 AiSeg 11 2.4 unpAIred 12 2.5 Limitations 12 3 Factors Affecting Image Quality in Computed Tomography 13 3.1 From CT Scan to Reconstruction 13 3.2 X-ray Tube and Focal Spot 14 3.3 Beam Hardening 14 3.4 Absorption, Scattering and Pairing 15 3.5 X-ray Detector 16 3.6 Geometric Calibration 17 3.7 Reconstruction Algorithm 17 3.8 Artifact corrections 18 4 On the Development of Augmentation Pipelines for Image Segmentation 19 4.0 Abstract 20 4.1 Introduction 20 4.2 Methods 21 4.2.1 Data Preparation 21 4.2.2 Augmentation 21 4.2.3 Networks 24 4.2.4 Training and Metrics 25 4.3 Experimental Design 26 4.3.1 Hardware 26 4.3.2 Workflow 26 4.3.3 Test on Cityscapes 26 4.4 Results and Discussion 26 4.4.1 Stage 1: Crating a Baseline 27 4.4.2 Stage 2: Using Offline Augmentation 27 4.4.3 Stage 3: Using Online Augmentation 27 4.4.4 Test on Cityscapes 29 4.4.5 Future Work – A New Online Augmentation 30 4.5 Conclusion 31 4.6 Appendix 31 4.6.1 Appendix A. List of All Networks 31 4.6.2 Appendix B. Augmentation Methods 32 4.6.3 Appendix C. Used RIWA Online Augmentation Parameters 36 4.6.4 Appendix D. Used Cityscapes Online Augmentation Parameters 36 4.6.5 Appendix E. Comparison of CNNs with best Backbones on RIWA 37 4.6.6 Appendix F. Segmentation Results 38 4.7 References 39 5 Comparison of 3D CNNs for Volume Segmentation 43 5.0 Abstract 44 5.1 Introduction 44 5.2 Datasets 44 5.2.1 Carbon Rovings 45 5.2.2 Concrete Pores 45 5.2.3 Polyethylene Fibers 45 5.2.4 Brain Mitochondria 45 5.2.5 Brain Tumor Segmentation Challenge (BraTS) 46 5.2.6 Head and Neck Cancer 46 5.3 Methods 46 5.3.1 Data Preprocessing 46 5.3.2 Hyperparameters 46 5.3.3 Metrics 47 5.3.4 Experimental Design 48 5.4 Results and Discussion 48 5.4.1 Impact of Initial Random States (Head and Neck Cancer Dataset) 48 5.4.2 Carbon Rovings 48 5.4.3 Concrete Pores 49 5.4.4 Polyethylene Fibers 49 5.4.5 Brain Mitochondria 50 5.4.6 BraTS 51 5.5 Conclusion 51 5.6 References 52 6 Segmentation of Carbon in CRC Using 3D Augmentation 55 6.0 Abstract 56 6.1 Introduction 56 6.2 Materials and Methods 58 6.2.1 Specimens 58 6.2.2 Microtomography 59 6.2.3 AI-Based Segmentation 60 6.2.4 Roving Extraction 64 6.2.5 Multiscale Modeling 65 6.2.6 Scaled Boundary Isogeometric Analysis 66 6.2.7 Parameterized RVE and Definition of Characteristic Geometric Properties 67 6.3 Results and Discussion 70 6.3.1 Microtomography 70 6.3.2 Deep Learning 71 6.3.3 Roving Extraction 74 6.3.4 Parameterized RVE and Definition of Characteristic Geometric Properties 75 6.4 Conclusion 79 6.5 References 80 7 Image-to-Image Translation for Semi-Supervised Semantic Segmentation 85 7.1 Introduction 85 7.2 Methods 86 7.2.1 Generative Adversarial Networks 87 7.2.2 Contrastive Unpaired Translation 87 7.2.3 Fréchet Inception Distance 89 7.2.4 Datasets 89 7.3 Experimental Design 92 7.4 Results and Discussion 94 7.4.1 Training and Inference of CUT 94 7.4.2 End-to-End Training for Semantic Segmentation 99 7.5 Conclusion 104 7.5.1 Future Work 104 8 Synthesis 107 8.1 Research Summary 107 8.1.1 Augmentation Pipelines 107 8.1.2 3D CNN Comparison 108 8.1.3 3D Data Augmentation for the Segmentation of Carbon Rovings 108 8.1.4 Synthetic Training Data Generation 109 8.2 Future Developments 109 8.2.1 Augmentation 109 8.2.2 Pre-trained 3D Encoder 111 8.2.3 On the Quality Control of Carbon Reinforced Concrete 111 8.2.4 Subvoxel Accurate Segmentation 113 8.2.5 Towards Volume-to-Volume Translation 114 8.3 Conclusion 114 References 117 List of Tables 125 List of Figures 127 List of Abbreviations 131 / Computertomographie (CT) bietet wertvolle Einblicke in die inneren Strukturen von Objekten und Organismen, was für Anwendungen von der Materialwissenschaft bis zur medizinischen Diagnostik von entscheidender Bedeutung ist. In CT-Daten ist ein Objekt durch eine 3D-Rekonstruktion dargestellt, die durch die Kombination mehrerer 2D-Röntgenbilder aus verschiedenen Winkeln um das Objekt herum erstellt wird. Jedes Voxel, ein Volumen Pixel, innerhalb des rekonstruierten Volumens stellt ein kleines kubisches Element dar und ermöglicht eine detaillierte räumliche Darstellung. Um aussagekräftige Informationen aus CT-Bilddaten zu extrahieren und eine Analyse und Interpretation zu ermöglichen, ist eine genaue Segmentierung der inneren Strukturen unerlässlich. Dies kann jedoch aufgrund verschiedener Artefakte, die durch die Physik eines CT-Scans und Eigenschaften des abgebildeten Objekts verursacht werden, eine Herausforderung darstellen. Diese Dissertation befasst sich direkt mit dieser Herausforderung, indem sie Techniken des Deep Learnings einsetzt. Konkret werden für die Segmentierung Convolutional Neural Networks (CNNs) verwendet, welche jedoch mit dem Problem begrenzter Trainingsdaten konfrontiert sind. Der Datenknappheit wird dabei durch Datenerweiterung begegnet, indem unbeaufsichtigt synthetische Trainingsdaten erzeugt und 2D- und 3D-Augmentierungssmethoden eingesetzt werden. Eine Kombination dieser Vervielfältigungsstrategien erlaubt eine Vereinfachung der Segmentierung in Voxeldaten und behebt effektiv die Datenknappheit. Im Wesentlichen zielt diese Arbeit darauf ab, das Training von CNNs zu vereinfachen, wobei wenige oder gar keine gelabelten Daten benötigt werden. Um die Ergebnisse dieser Arbeit Forschenden zugänglicher zu machen, wurden zwei benutzerfreundliche Softwarelösungen, unpAIred und AiSeg, entwickelt. Diese ermöglichen die Generierung von Trainingsdaten, die Augmentierung sowie das Training, die Analyse und die Anwendung von CNNs. In dieser kumulativen Arbeit werden zunächst einfachere, aber effiziente konventionelle Methoden zur Datenvervielfältigung untersucht, wie z. B. radiometrische und geometrische Bildmanipulationen, die bereits häufig in der Literatur verwendet werden. Diese Methoden werden jedoch in der Regel zufällig nacheinander angewandt und folgen keiner bestimmten Reihenfolge. Der Schwerpunkt des ersten Forschungsartikels liegt darin, diesen Ansatz zu untersuchen und sowohl Online- als auch Offline-Datenerweiterungspipelines zu entwickeln, die eine systematische Sequenzierung dieser Operationen ermöglichen. Bei der Offline Variante werden die auf der Festplatte gespeicherten Trainingsdaten vervielfältigt, während die Online-Erweiterung dynamisch zur Laufzeit erfolgt, kurz bevor die Bilder dem CNN gezeigt werden. Es wird erfolgreich gezeigt, dass eine zufällige Verkettung von geometrischen und radiometrischen Methoden den neuen Pipelines unterlegen ist. Anschließend wird ein Vergleich von 3D-CNNs durchgeführt, um die optimalen Modelle für Segmentierungsaufgaben zu identifizieren, wie z.B. die Segmentierung von Carbonbewehrung und Luftporen in CT-Scans von carbonverstärktem Beton (CRC). Durch die Bewertung von acht 3D-CNN-Modellen auf sechs Datensätzen werden Empfehlungen für die Auswahl des genauesten Modells auf der Grundlage der Datensatzeigenschaften gegeben. Die Analyse unterstreicht die konstante Überlegenheit des 3D UNets, eines der CNNs, und seiner Residualversion bei Segmentierung von Rovings (Carbonfaserbündel) und Poren. Aufbauend auf den 2D Augmentierungspipelines und den Ergebnissen des 3D-CNN-Vergleichs werden die Pipelines auf die dritte Dimension erweitert, um insbesondere die Segmentierung der Carbonbewehrung in CT-Scans von CRC zu ermöglichen. Eine vergleichende Analyse verschiedener 3D Augmentierungsstrategien, die sowohl Offline- als auch Online-Erweiterungsvarianten umfassen, gibt Aufschluss über deren Effektivität. Die Offline-Augmentierung führt zwar zu weniger Artefakten, kann aber nur Rovings segmentieren, die bereits in den Trainingsdaten vorhanden sind. Die Online-Augmentierung erweist sich hingegen als unerlässlich für die effektive Segmentierung von Carbon-Roving-Typen, die nicht im Datensatz enthalten sind. Einschränkungen wie die geringe Vielfalt des Datensatzes und eine zu aggressive Online-Datenerweiterung, die zu Segmentierungsartefakten führt, erfordern jedoch weitere Methoden, um die Datenknappheit zu beheben. In Anbetracht der Notwendigkeit eines größeren und vielfältigeren Datensatzes erweitert diese Arbeit die Ergebnisse der drei Forschungsartikel durch die Einführung einer auf Deep Learning basierenden Augmentierung, die ein Generative Adversarial Network (GAN), genannt Contrastive Unpaired Translation (CUT), zur Erzeugung synthetischer Trainingsdaten verwendet. Durch die Kombination des GANs mit den Augmentierungspipelines wird eine halbüberwachte Ende-zu-Ende-Trainingsmethode vorgestellt und die erfolgreiche Erzeugung von Trainingsdaten für die 2D-Porensegmentierung demonstriert. Es bestehen jedoch noch Herausforderungen bei der Implementierung einer stabilen 3D-CUT-Version, was weitere Forschungs- und Entwicklungsanstrengungen erfordert. Zusammenfassend adressieren die Ergebnisse dieser Dissertation Herausforderungen der CT-Datensegmentierung in der Materialwissenschaft, die durch Deep-Learning-Techniken und neuartige 2D- und 3D-Online- und Offline-Augmentierungspipelines gelöst werden. Durch die Evaluierung verschiedener 3D-CNN-Modelle werden maßgeschneiderte Empfehlungen für spezifische Segmentierungsaufgaben gegeben. Darüber hinaus zeigen Untersuchungen zur Deep Learning basierten Augmentierung mit CUT vielversprechende Ergebnisse bei der Generierung synthetischer Trainingsdaten. Zukünftige Arbeiten umfassen die Entwicklung einer stabilen Implementierung einer 3D-CUT-Version, die Erforschung neuer Modellarchitekturen und die Entwicklung von subvoxelgenauen Segmentierungstechniken. Diese haben das Potenzial für bedeutende Fortschritte bei der Segmentierung in Tomographiedaten.:Abstract IV Zusammenfassung VI 1 Introduction 1 1.1 Thesis Structure 2 1.2 Scientific Context 3 1.2.1 Developments in the Segmentation in Tomography Data 3 1.2.2 3D Semantic Segmentation using Machine Learning 5 1.2.3 Data Augmentation 6 2 Developed Software Solutions: AiSeg and unpAIred 9 2.1 Software Design 10 2.2 Installation 11 2.3 AiSeg 11 2.4 unpAIred 12 2.5 Limitations 12 3 Factors Affecting Image Quality in Computed Tomography 13 3.1 From CT Scan to Reconstruction 13 3.2 X-ray Tube and Focal Spot 14 3.3 Beam Hardening 14 3.4 Absorption, Scattering and Pairing 15 3.5 X-ray Detector 16 3.6 Geometric Calibration 17 3.7 Reconstruction Algorithm 17 3.8 Artifact corrections 18 4 On the Development of Augmentation Pipelines for Image Segmentation 19 4.0 Abstract 20 4.1 Introduction 20 4.2 Methods 21 4.2.1 Data Preparation 21 4.2.2 Augmentation 21 4.2.3 Networks 24 4.2.4 Training and Metrics 25 4.3 Experimental Design 26 4.3.1 Hardware 26 4.3.2 Workflow 26 4.3.3 Test on Cityscapes 26 4.4 Results and Discussion 26 4.4.1 Stage 1: Crating a Baseline 27 4.4.2 Stage 2: Using Offline Augmentation 27 4.4.3 Stage 3: Using Online Augmentation 27 4.4.4 Test on Cityscapes 29 4.4.5 Future Work – A New Online Augmentation 30 4.5 Conclusion 31 4.6 Appendix 31 4.6.1 Appendix A. List of All Networks 31 4.6.2 Appendix B. Augmentation Methods 32 4.6.3 Appendix C. Used RIWA Online Augmentation Parameters 36 4.6.4 Appendix D. Used Cityscapes Online Augmentation Parameters 36 4.6.5 Appendix E. Comparison of CNNs with best Backbones on RIWA 37 4.6.6 Appendix F. Segmentation Results 38 4.7 References 39 5 Comparison of 3D CNNs for Volume Segmentation 43 5.0 Abstract 44 5.1 Introduction 44 5.2 Datasets 44 5.2.1 Carbon Rovings 45 5.2.2 Concrete Pores 45 5.2.3 Polyethylene Fibers 45 5.2.4 Brain Mitochondria 45 5.2.5 Brain Tumor Segmentation Challenge (BraTS) 46 5.2.6 Head and Neck Cancer 46 5.3 Methods 46 5.3.1 Data Preprocessing 46 5.3.2 Hyperparameters 46 5.3.3 Metrics 47 5.3.4 Experimental Design 48 5.4 Results and Discussion 48 5.4.1 Impact of Initial Random States (Head and Neck Cancer Dataset) 48 5.4.2 Carbon Rovings 48 5.4.3 Concrete Pores 49 5.4.4 Polyethylene Fibers 49 5.4.5 Brain Mitochondria 50 5.4.6 BraTS 51 5.5 Conclusion 51 5.6 References 52 6 Segmentation of Carbon in CRC Using 3D Augmentation 55 6.0 Abstract 56 6.1 Introduction 56 6.2 Materials and Methods 58 6.2.1 Specimens 58 6.2.2 Microtomography 59 6.2.3 AI-Based Segmentation 60 6.2.4 Roving Extraction 64 6.2.5 Multiscale Modeling 65 6.2.6 Scaled Boundary Isogeometric Analysis 66 6.2.7 Parameterized RVE and Definition of Characteristic Geometric Properties 67 6.3 Results and Discussion 70 6.3.1 Microtomography 70 6.3.2 Deep Learning 71 6.3.3 Roving Extraction 74 6.3.4 Parameterized RVE and Definition of Characteristic Geometric Properties 75 6.4 Conclusion 79 6.5 References 80 7 Image-to-Image Translation for Semi-Supervised Semantic Segmentation 85 7.1 Introduction 85 7.2 Methods 86 7.2.1 Generative Adversarial Networks 87 7.2.2 Contrastive Unpaired Translation 87 7.2.3 Fréchet Inception Distance 89 7.2.4 Datasets 89 7.3 Experimental Design 92 7.4 Results and Discussion 94 7.4.1 Training and Inference of CUT 94 7.4.2 End-to-End Training for Semantic Segmentation 99 7.5 Conclusion 104 7.5.1 Future Work 104 8 Synthesis 107 8.1 Research Summary 107 8.1.1 Augmentation Pipelines 107 8.1.2 3D CNN Comparison 108 8.1.3 3D Data Augmentation for the Segmentation of Carbon Rovings 108 8.1.4 Synthetic Training Data Generation 109 8.2 Future Developments 109 8.2.1 Augmentation 109 8.2.2 Pre-trained 3D Encoder 111 8.2.3 On the Quality Control of Carbon Reinforced Concrete 111 8.2.4 Subvoxel Accurate Segmentation 113 8.2.5 Towards Volume-to-Volume Translation 114 8.3 Conclusion 114 References 117 List of Tables 125 List of Figures 127 List of Abbreviations 131

Denna uppsats presenterar resultaten av en kvalitativ undersökning som syftar till att ge en djupare förståelse för användningen av AI inom radiologi, dess framtida påverkan på yrket och hur det används idag. Genom att genomföra tre intervjuer med personer som arbetar inom radiologi, har datainsamlingen fokuserat på att identifiera de positiva och negativa aspekterna av AI i radiologi, samt dess potentiella konsekvenser på yrket. Resultaten visar på en allmän acceptans för AI inom radiologi och dess förmåga att förbättra diagnostiska processer och effektivisera arbetet. Samtidigt finns det en viss oro för att AI kan ersätta människor och minska behovet av mänskliga bedömningar. Denna uppsats ger en grundläggande förståelse för hur AI används inom radiologi och dess möjliga framtida konsekvenser. / This essay presents the results of a qualitative study aimed at gaining a deeper understanding of the use of artificial intelligence (AI) in radiology, its potential impact on the profession and how it’s used today. By conducting three interviews with individuals working in radiology, data collection focused on identifying the positive and negative aspects of AI in radiology, as well as its potential consequences on the profession. The results show a general acceptance of AI in radiology and its ability to improve diagnostic processes and streamline work. At the same time, there is a certain concern that AI may replace humans and reduce the need for human judgments. This report provides a basic understanding of how AI is used in radiology and its possible future consequences.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Objetivo: O desenvolvimento da tecnologia em diagnóstico odontológico por imagem através dos Tomógrafos Computadorizados por Feixe Cônico, tornou possível e acessível a avaliação cefalométrica através de reconstruções volumétricas do crânio. Parâmetros baseados em evidências científicas são necessários para implementar o seu uso. Alguns parâmetros utilizados na cefalometria convencional (bidimensional) deverão ser esquecidos, outros deverão ser adaptados, outros, ainda, deverão ser criados. Propomos aqui a criação de um Cefalostato Virtual para orientação do crânio em TC, com a utilização de pontos intracranianos, que são mais estáveis. Também propomos a criação do ponto TS e da linha TS-Pg em substituição ao ponto S e ao eixo Y de crescimento de Downs, respectivamente. Além disso, propomos a linha Ba-Op como referência para casos de assimetria faciais onde não é possível a utilização do plano Horizontal de Frankfurt, em casos, por exemplo, de síndromes que afetem os pontos de referências mais externos. Métodos: 49 crânios pertencentes ao do Museu de Anatomia UNIFESP, foram escaneados em um tomógrafo computadorizado por feixe cônico (TCFC), na clínica ISOOrthographic, São Paulo. As pontuações foram realizadas em dois momentos, com espaçamento de uma semana. Foram calculadas estatisticamente medidas-resumo (média, quartis, mínimo, máximo e desvio padrão). Foram calculadas também as correlações intraclasse e correlações de Pearson entre o Eixo Y (S-Gn) e linha entre os pontos TS e Pg. Resultados: Apesar de se observar uma baixa reprodutibilidade nas coordenadas, para os pontos CE, Pg e Gn, foi observada alta correlação entre as medidas angulares em questão. Para descrever a inclinação do Eixo Y em função da inclinação da Linha TS e Pg adotou-se um modelo de regressão linear simples descrito pela equação abaixo: Ang Sö- Gn = 0,989 Ang TS Pgi i Conclusões: o uso do Cefalostato Virtual na orientação de Crânios em Tomografia Computadorizada é factível e favorece a reprodução do posicionamento craniano; apesar da baixa reprodutibilidade intra observador dos pontos CE, Pg e Gn, novos critérios tridimensionais na definição destes pontos poderiam aumentar a precisão na sua localização; a alta reprodutibilidade intra observador para os pontos Op, TS e N, sugere que os critérios anatômicos próprios das estruturas estudadas favorecem a sua determinação; o ponto TS apresentou maior reprodutibilidade do que o ponto S, embora esta diferença não tenha sido estatisticamente significante, podendo-se substituir o ponto S pelo TS em estudos futuros; existe alta correlação entre a linha entre os pontos TS e Pg e o Eixo Y; a avaliação do comportamento da inclinação da linha orbitomeática (HF) com relação à linha Básio-Opístio sugere que na presença de alterações cranianas este relacionamento propicie auxílio no diagnóstico das alterações craniofaciais. / Objective: The development of new technology in dental diagnosis by cone beam CT (CBCT) image, made possible and accessible the realization of cephalometric evaluation through volumetric reconstructions of the skull. Scientific parameters with evidence-based are needed to implement its use. Some parameters used in conventional cephalometry (2D) maybe need to be forgotten, others should be adapted, and others still to be created. In this research we propose to create a Virtual Cephalostat orientation of the skull in CT, with the intracranial landmarks, because they are more stable. We propose the creation of landmark TS (Tubercle Sella) and the TS-Pg line to replace the landmark S (Sella) and the Y-axis of growth (Downs), respectively. Furthermore, we propose to use the Basion-Opistion line as a reference for cases of craniofacial asymmetry where is not possible to use the Frankfurt horizontal plane, as in some cases of syndromes that affects the most external landmarks. Methods: 49 skulls of Anatomy Museum of UNIFESP Federal University of São Paulo, were scanned in a CBCT. The analyses were performed in 2 stages, within 1-week space. Statistics measurements were calculated (mean, quartiles, minimum, maximum and standard deviation). We also calculated the intraclass correlations (ICC) and the Pearson correlations between the Y axis (S-Gn) and the line between landmarks TS-Pg. Results: Even if there is a low reproducibility in the coordinates for landmarks EC (Ethmoidal Crest), Pg and Gn it was observed a high correlation between the angular measures in question. To describe the inclination of the Y axis according to the slope of the line adopted TS and Pg a simple linear regression model is used, showed by the equation bellow: Ang Sö- Gn = 0,989 Ang TS Pgi i Conclusions: The use of the Virtual Cephalostat in orientation of skulls using CBCT is feasible and facilitates the reproduction of the skull position, despite the low intra observer reproducibility of landmarks EC, Pg and Gn, new 3D criteria in the definition of these landmarks could increase the precision in its location. The high intra observer reproducibility at the landmarks Op, N and TS, suggests that the anatomical criteria themselves promote their reliability; The TS landmark showed a higher reproducibility than the S landmark, even though the difference was not statistically significant, and it should be replaced by the landmark TS in future studies. There is a high correlation between the TS - Pg line and Y-axis. The relationship between the slope of the HF plane and Ba -Op line suggests that in the presence of the alteration of morphology in craniofacial structure, this relationship offer help in the diagnosis of craniofacial changes.

Die Lebendspende von Leberlappen wird in der Zukunft gerade vor dem Hintergrund des stets weiter steigenden Bedarfs und des sich dazu diskrepant entwickelnden Mangels an Leichenorganen zur Transplantation eine zunehmend wichtige Rolle einnehmen, um Patienten im Endstadium einer Lebererkrankung kurativ zu versorgen. Umso mehr spielen Überlegungen zur Gewährleistung insbesondere der Sicherheit für einen gesunden Lebendspender eine Rolle, ohne Risiken für ihn eliminieren zu können. In diese Überlegungen gehen Weiterentwicklungen der Möglichkeiten für die spezielle Evaluation der Leber eines potenziellen Spenders anhand bildgebender Verfahren ein. Hier nehmen Methoden zur präoperativen Abschätzung der Gewichts- und Volumenverhältnisse einer potenziellen Spenderleber und ihrer Lappen einen besonderen Stellenwert ein, da bei entsprechend ungünstigen Voraussetzungen ein gesunder Mensch aus Gründen der Sicherheit für eine Lebendspende nicht in Frage kommt. Die vorliegende Arbeit zeigt anhand einer prospektiven Studie unterschiedliche Methoden der präoperativen CT-gestützten Volumetrie zur Evaluation von Lebern und ihrer beiden Lappen von potenziellen Lebendspendern auf. Dabei wurde ein neu entwickeltes Volumetrieverfahren klinisch erprobt und mit einem etablierten Verfahren verglichen. Als Referenzgrößen wurden erstmalig gleichermaßen intraoperativ gemessene Gewichte und Volumina der transplantieren rechten Leberlappen herangezogen. Hinsichtlich der auf CT-gestützter Volumetrie basierenden, präoperativen Abschätzung von intraoperativ zu erwartendem Gewicht und Volumen von rechten Leberlappen im Rahmen einer Lebendspende erwies sich das etablierte Verfahren bezüglich des Gewichts dem neu entwickelten Verfahren geringgradig überlegen, während das neu entwickelte Verfahren bezüglich des Volumens gegenüber dem etablierten Verfahren geringgradig besser abschnitt. Darüber hinaus resultierte aus den intraoperativ erhobenen Daten die Erkenntnis, dass die physikalische Dichte von gesundem Lebergewebe bei einer relativ hohen interindividuellen Streuung im Mittel um knapp 12% höher liegt als zumeist angenommen. In Zukunft werden Fortschritte technischer Verfahren sehr genaue virtuelle Trennungen von Lebern in ihre beiden Lappen ermöglichen. Gleichzeitig werden chirurgische Resektionstechniken verfeinert. Sowohl der virtuelle als auch der reale Ansatz haben den Anspruch, die avaskuläre und somit ideale Resektionsfläche zwischen beiden Leberlappen aufzusuchen, um gleichzeitig präoperativ exakte Gewichts- und Volumenabschätzungen zu ermöglichen und intraoperativ Risiken zu minimieren. Welchem dieser beiden Ansätze die stärkste Annäherung an diesen Anspruch oder dessen Vollendung zuerst gelingt, wird sich als Referenzmethode behaupten, an der sich der unterlegene Ansatz wird messen lassen müssen. / The increasing need of cadaveric liver grafts and the scarcity of living related liver transplants (LRLT) will play a critical role in the future treatment of patients suffering from end stage liver disease. Various considerations, including especially a safe outcome for the donor, are essential. However, risks can not be eliminated. These considerations can be influenced in the evaluation of a potential living donor. Accurate methods, including imaging modalities, for the preoperative estimation of the potential donor liver’s weight and volume are essential as an adverse condition would preclude a living donation for safety reasons. This thesis presents different methods of preoperative CT-based volumetric analyses for the evaluation the liver and both its lobes in potential living donors. A newly developed method of volumetric analysis was clinically tested and compared with an established method. Intraoperatively measured weights and volumes of transplanted right hepatic lobes were used as reference values. With regards to the weight, the established method proved to be mildly superior, while the newer method was slightly more accurate for volume. Additionally, it was discovered that the mean density of healthy liver tissue is approximately 12 percent higher than generally assumed but with a relatively high individual variation. Progress in technical methods will render possible very exact virtual divisions of the liver in both of its lobes. Both the virtual and surgical approach have a claim for finding the appropriate avascular and consequently ideal resection plane in order to minimize risks intraoperatively.

Objetivo: O desenvolvimento da tecnologia em diagnóstico odontológico por imagem através dos Tomógrafos Computadorizados por Feixe Cônico, tornou possível e acessível a avaliação cefalométrica através de reconstruções volumétricas do crânio. Parâmetros baseados em evidências científicas são necessários para implementar o seu uso. Alguns parâmetros utilizados na cefalometria convencional (bidimensional) deverão ser esquecidos, outros deverão ser adaptados, outros, ainda, deverão ser criados. Propomos aqui a criação de um Cefalostato Virtual para orientação do crânio em TC, com a utilização de pontos intracranianos, que são mais estáveis. Também propomos a criação do ponto TS e da linha TS-Pg em substituição ao ponto S e ao eixo Y de crescimento de Downs, respectivamente. Além disso, propomos a linha Ba-Op como referência para casos de assimetria faciais onde não é possível a utilização do plano Horizontal de Frankfurt, em casos, por exemplo, de síndromes que afetem os pontos de referências mais externos. Métodos: 49 crânios pertencentes ao do Museu de Anatomia UNIFESP, foram escaneados em um tomógrafo computadorizado por feixe cônico (TCFC), na clínica ISOOrthographic, São Paulo. As pontuações foram realizadas em dois momentos, com espaçamento de uma semana. Foram calculadas estatisticamente medidas-resumo (média, quartis, mínimo, máximo e desvio padrão). Foram calculadas também as correlações intraclasse e correlações de Pearson entre o Eixo Y (S-Gn) e linha entre os pontos TS e Pg. Resultados: Apesar de se observar uma baixa reprodutibilidade nas coordenadas, para os pontos CE, Pg e Gn, foi observada alta correlação entre as medidas angulares em questão. Para descrever a inclinação do Eixo Y em função da inclinação da Linha TS e Pg adotou-se um modelo de regressão linear simples descrito pela equação abaixo: Ang Sö- Gn = 0,989 Ang TS Pgi i Conclusões: o uso do Cefalostato Virtual na orientação de Crânios em Tomografia Computadorizada é factível e favorece a reprodução do posicionamento craniano; apesar da baixa reprodutibilidade intra observador dos pontos CE, Pg e Gn, novos critérios tridimensionais na definição destes pontos poderiam aumentar a precisão na sua localização; a alta reprodutibilidade intra observador para os pontos Op, TS e N, sugere que os critérios anatômicos próprios das estruturas estudadas favorecem a sua determinação; o ponto TS apresentou maior reprodutibilidade do que o ponto S, embora esta diferença não tenha sido estatisticamente significante, podendo-se substituir o ponto S pelo TS em estudos futuros; existe alta correlação entre a linha entre os pontos TS e Pg e o Eixo Y; a avaliação do comportamento da inclinação da linha orbitomeática (HF) com relação à linha Básio-Opístio sugere que na presença de alterações cranianas este relacionamento propicie auxílio no diagnóstico das alterações craniofaciais. / Objective: The development of new technology in dental diagnosis by cone beam CT (CBCT) image, made possible and accessible the realization of cephalometric evaluation through volumetric reconstructions of the skull. Scientific parameters with evidence-based are needed to implement its use. Some parameters used in conventional cephalometry (2D) maybe need to be forgotten, others should be adapted, and others still to be created. In this research we propose to create a Virtual Cephalostat orientation of the skull in CT, with the intracranial landmarks, because they are more stable. We propose the creation of landmark TS (Tubercle Sella) and the TS-Pg line to replace the landmark S (Sella) and the Y-axis of growth (Downs), respectively. Furthermore, we propose to use the Basion-Opistion line as a reference for cases of craniofacial asymmetry where is not possible to use the Frankfurt horizontal plane, as in some cases of syndromes that affects the most external landmarks. Methods: 49 skulls of Anatomy Museum of UNIFESP Federal University of São Paulo, were scanned in a CBCT. The analyses were performed in 2 stages, within 1-week space. Statistics measurements were calculated (mean, quartiles, minimum, maximum and standard deviation). We also calculated the intraclass correlations (ICC) and the Pearson correlations between the Y axis (S-Gn) and the line between landmarks TS-Pg. Results: Even if there is a low reproducibility in the coordinates for landmarks EC (Ethmoidal Crest), Pg and Gn it was observed a high correlation between the angular measures in question. To describe the inclination of the Y axis according to the slope of the line adopted TS and Pg a simple linear regression model is used, showed by the equation bellow: Ang Sö- Gn = 0,989 Ang TS Pgi i Conclusions: The use of the Virtual Cephalostat in orientation of skulls using CBCT is feasible and facilitates the reproduction of the skull position, despite the low intra observer reproducibility of landmarks EC, Pg and Gn, new 3D criteria in the definition of these landmarks could increase the precision in its location. The high intra observer reproducibility at the landmarks Op, N and TS, suggests that the anatomical criteria themselves promote their reliability; The TS landmark showed a higher reproducibility than the S landmark, even though the difference was not statistically significant, and it should be replaced by the landmark TS in future studies. There is a high correlation between the TS - Pg line and Y-axis. The relationship between the slope of the HF plane and Ba -Op line suggests that in the presence of the alteration of morphology in craniofacial structure, this relationship offer help in the diagnosis of craniofacial changes.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal