Accurate, cross-scanner assessment of in-vivo air density used to quantitatively assess amount and distribution of emphysema in COPD subjects has remained elusive. Hounsfield units (HU) within tracheal air can be considerably more positive than -1000 HU. With the advent of new dual-source scanners which employ dedicated scatter correction techniques, it is of interest to evaluate how the quantitative measures of lung density compare between dual-source and single-source scan modes. This study has sought to characterize in-vivo and phantom-based air metrics using dual-energy computed tomography technology where the nature of the technology has required adjustments to scatter correction. Anesthetized ovine (N=6), swine (N=13: more human-like rib cage shape), lung phantom and a thoracic phantom were studied using a dual-source MDCT scanner (Siemens Definition Flash. Multiple dual-source dual-energy (DSDE) and single-source (SS) scans taken at different energy levels and scan settings were acquired for direct quantitative comparison. Density histograms were evaluated for the lung, tracheal, water and blood segments. Image data were obtained at 80, 100, 120, and 140 kVp in the SS mode (B35f kernel) and at 80, 100, 140, and 140-Sn (tin filtered) kVp in the DSDE mode (B35f and D30f kernels), in addition to variations in dose, rotation time, and pitch. To minimize the effect of cross-scatter, the phantom scans in the DSDE mode was obtained by reducing the tube current of one of the tubes to its minimum (near zero) value. When using image data obtained in the DSDE mode, the median HU values in the tracheal regions of all animals and the phantom were consistently closer to -1000 HU regardless of reconstruction kernel (chapters 3 and 4). Similarly, HU values of water and blood were consistently closer to their nominal values of 0 HU and 55 HU respectively. When using image data obtained in the SS mode the air CT numbers demonstrated a consistent positive shift of up to 35 HU with respect to the nominal -1000 HU value. In vivo data demonstrated considerable variability in tracheal, influenced by local anatomy with SS mode scanning while tracheal air was more consistent with DSDE imaging. Scatter effects in the lung parenchyma differed from adjacent tracheal measures. In summary, data suggest that enhanced scatter correction serves to provide more accurate CT lung density measures sought to quantitatively assess the presence and distribution of emphysema in COPD subjects. Data further suggest that CT images, acquired without adequate scatter correction, cannot be corrected by linear algorithms given the variability in tracheal air HU values and the independent scatter effects on lung parenchyma.

Aufgrund der unterschiedlichen Behandlungsstrategien ist es wichtig, Patienten im großen Kollektiv des akuten respiratorischen Versagens frühzeitig und zuverlässig der richtigen Diagnosegruppe zuzuordnen. Bezüglich der interstitiellen Lungenerkrankungen (ILD) ist die Diagnosestellung trotz intensiver Forschungsbemühungen noch immer schwierig und lässt sich meist nur mit einer Lungenbiopsie abschließend klären. Eine Biopsie stellt einen invasiven Eingriff dar, birgt Risiken und kann nicht bei jedem Patienten durchgeführt werden. Deshalb wäre die Etablierung eines alternativen Diagnostikums, ohne zusätzliche Risiken für den Patienten, wünschenswert. Da sich die quantitative Computertomographie (qCT) bei anderen Lungenerkrankungen bewährt hat, wurde in der vorliegenden Arbeit überprüft, ob die qCT in Bezug auf ILD relevante Informationen liefern kann. Im hier untersuchten Kollektiv fanden sich sowohl Patienten mit ILD als auch Patienten mit nicht interstitiellen Lungenerkrankungen (nILD). Beide Gruppen wurden miteinander und mit einer lungengesunden Vergleichsgruppe verglichen. Ebenso wurde das Kollektiv in überlebende und nicht überlebende Patienten unterteilt und analysiert. Basierend auf den zugrundeliegenden pathophysiologischen Konzepten wurde in der vorliegenden Arbeit mittels qCT-Analyse untersucht, ob sich die unterschiedlichen Prozesse in Veränderungen Lungengewichts widerspiegeln. Entgegen der Hypothesen fanden wir in der vorliegenden Arbeit keine signifikanten Lungengewichtsunterschiede zwischen ILD- und nILD-Patienten, weder zum Aufnahmezeitpunkt, noch im Verlauf des stationären Aufenthalts. Beide Gruppen unterscheiden sich jedoch signifikant vom Lungengewicht gesunder Patienten. In Bezug auf die Letalität unterscheiden sich beide Gruppen jedoch deutlich: In der ILD-Gruppe verstarben acht von neun Patienten (89%), in der nILD-Gruppe vier von 18 (22%) . Die Lungengewichtsverläufe zwischen Überlebenden und nicht Überlebenden beschrieben einen Trend: Nicht Überlebende hatten ein höheres Lungengewicht als Überlebende. Hier zeigte sich ein statistisch signifikanter Unterschied. In Zusammenschau dieser Ergebnisse und denen anderer Studien erhärten sich die Hinweise darauf, dass das Lungengewicht bedingt als prognostischer Marker dienen kann.

The characterization of the normal pulmonary acinus is a necessary first step in understanding the nature of respiratory physiology and in assessing the etiology of pulmonary pathology. Murine models play a vital role in the advancement of current understanding of the dynamics of gas exchange, particle deposition and the manifestations of diseases such as COPD, Cystic Fibrosis and Asthma. With the advent of interior tomography techniques, high-resolution micro computed tomography (μCT) systems provide the ability to nondestructively assess the pulmonary acinus at micron and sub-micron resolutions. With the application of Systematic Uniform Random Sampling (SURS) principles applied to in-situ fixed, intact, ex-vivo lungs, we seek to characterize the structure of pulmonary acini in mice and study the variations across dimensions of age, location within the lung and strain phenotypes. Lungs from mice of three common research strains were perfusion fixed in-situ, and imaged using a multi-resolution μCT system (Micro XCT 400, Zeiss Inc.). Using lower resolution whole lung images, SURS methods were used for identification of region-specific acini for high-resolution imaging. Acinar morphometric metrics included diameters, lengths and branching angles for each alveolar duct and total path lengths from entrance of the acinus to the terminal alveolar sacs. In addition, other metrics such as acinar volume, alveolar surface area and surface area/volume ratios were assessed. A generation-based analysis demonstrated significant differences in acinar morphometry across young and old age groups and across the three strains. The method was successfully adapted to large animals and the data from one porcine specimen has been presented. The registration framework provides a direct technique to assess acinar deformations and provides critical physiological information about the state of alveolar ducts and individual alveoli at different phases of respiration. The techniques presented here allow us to perform direct assessment of the three-dimensional structure of the pulmonary acinus in previously unavailable detail and present a unique technique for comprehensive quantitative analysis. The acinar morphometric parameters will help develop improved mathematical and near-anatomical models that can accurately represent the geometric structure of acini, leading to improved assessment of flow dynamics in the normal lung.

L'ostéoporose est une maladie caractérisée par la perte de la masse osseuse et la dégradation de la micro-architecture osseuse. Bien que l'ostéoporose ne soit pas une maladie mortelle, les fractures qu'elle provoque peuvent entraîner de graves complications (lésions des vaisseaux et des nerfs, infections, raideur), parfois accompagnées de menaces de mort. La micro-architecture osseuse joue un rôle important dans le diagnostic de l'ostéoporose. Deux appareils de tomodensitométrie courants pour scanner la micro-architecture osseuse sont la tomodensitométrie quantitative périphérique à haute résolution et la tomodensitométrie microscopique. Le premier dispositif donne accès à l'investigation in vivo, mais sa résolution spatiale est inférieure. Le micro tomodensitomètre donne une meilleure résolution spatiale, mais il est contraint à une mesure ex vivo. Dans cette thèse, notre but est d'améliorer la résolution spatiale des images de tomodensitométrie périphérique à haute résolution afin que l'analyse quantitative des images résolues soit proche de celle donnée par les images de tomodensitométrie Micro. Nous sommes partis de la régularisation de la variation totale, à une combinaison de la variation totale et du potentiel de double puits pour améliorer le contraste des résultats. Ensuite, nous envisageons d'utiliser la méthode d'apprentissage par dictionnaire pour récupérer plus de détails sur la structure. Par la suite, une méthode d'apprentissage approfondi a été proposée pour résoudre un problème de super résolution et de segmentation joint. Les résultats montrent que la méthode d'apprentissage profond est très prometteuse pour les applications futures. / Osteoporosis is a disease characterized by loss of bone mass and degradation of bone microarchitecture. Although osteoporosis is not a fatal disease, the fractures it causes can lead to serious complications (damage to vessels and nerves, infections, stiffness), sometimes accompanied with risk of death. The bone micro-architecture plays an important role for the diagnosis of osteoporosis. Two common CT devices to scan bone micro architecture is High resolution-peripheral Quantitative CT and Micro CT. The former device gives access to in vivo investigation, but its spatial resolution is inferior. Micro CT gives better spatial resolution, but it is constrained to ex vivo measurement. In this thesis, we attempt to improve the spatial resolution of high resolution peripheral CT images so that the quantitative analysis of the resolved images is close to the one given by Micro CT images. We started from the total variation regularization, to a combination of total variation and double-well potential to enhance the contrast of results. Then we consider to use dictionary learning method to recover more structure details. Afterward, a deep learning method has been proposed to solve a joint super resolution and segmentation problem. The results show that the deep learning method is very promising for future applications.