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.

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / A anÃlise de imagens mÃdicas por meio de tÃcnicas de visÃo computacional tornou-se bastante promissora, principalmente pelo fato de aperfeiÃoar a acurÃcia diagnÃstica de diversas patologias. Por essas razÃo, a Pneumologia à considerada atualmente uma Ãrea de concentraÃÃo de projetos que envolvem mÃtodos de Processamento Digital de Imagens. A segmentaÃÃo de vasos sanguÃneos pulmonares à de bastante auxÃlio na detecÃÃo de cardiopatias pulmonares. Esse processo à realizado atravÃs da anÃlise dos resultados obtidos por exame de diagnÃstico por imagem, os quais se destacam as radiografias torÃcicas, tomografia computadorizada (TC) do tÃrax, ressonÃncia magnÃtica, cintilografia pulmonar e angiografia. A hipertensÃo pulmonar e o cÃncer sÃo exemplos de doenÃas que podem ser diagnosticadas com menor subjetividade ao realizar a segmentaÃÃo de vasos, visualizaÃÃo em trÃs dimensÃes e extraÃÃo de seus atributos. Devido a essa importÃncia, diversos algoritmos sÃo desenvolvidos com intuito de obter uma segmentaÃÃo Ãtima destas estruturas. Dentre estes, encontram-se os mÃtodos por contornos ativos, LÃgica Fuzzy, Crescimento de RegiÃes, Filtragem Multi-escalar 3D e algoritmo Expectation Maximization (EM). Nesta dissertaÃÃo, sÃo segmentados os vasos sanguÃneos pulmonares de imagens de tomografia computadorizada do tÃrax utilizando-se trÃs mÃtodos: uma combinaÃÃo de Crescimento de RegiÃes 3D controlado por uma funÃÃo de pertinÃncia gaussiana e limiarizaÃÃo; um mÃtodo hÃbrido de segmentaÃÃo por Conectividade Fuzzy e limiarizaÃÃo; por m, a segmentaÃÃo utilizando o classicador K-mÃdias. Os resultados obtidos pelas segmentaÃÃes sÃo analisados e comparados por meio de uma anÃlise dos coecientes de similaridade e sensibilidade. Os resultados da aplicaÃÃo dos trÃs mÃtodos sÃo caracterizados aceitÃveis e compatÃveis com os observados na literatura. / Medical image analysis using computer vision techniques has become quite promising because of its improvement on the diagnostic accuracy of various pathologies. For this reason, pulmonology became an area of high concentration of projects involving methods of Digital Image Processing. The blood vessels segmentation in the lung is an important aid in the detection of pulmonary heart diseases. This process is performed by analyzing the results obtained with known diagnostic imaging exams, like chest Xrays, computed tomography (CT) scan, magnetic resonance imaging, scintigraphy and angiography. Pulmonary hypertension and cancer are examples of diseases that can be diagnosed with less subjectivity if performing vessels segmentation, three-dimensional visualization and attribute extraction of these images. Thus, several algorithms are developed with the objective of obtaining an optimal segmentation of these structures. Among those algorithms are active contours, fuzzy logic, 3D Region Growing, 3D multi-scale ltering algorithm and Expectation Maximization (EM). In this study, the blood vessels were extracted from lung CT scans of the chest using three methods. The rst is a combination of 3D Region Growing controlled by a Gaussian membership function and thresholding, the second is a hybrid segmentation by thresholding and Fuzzy Connectedness. Finally,the third refers to segmentation using the K-means classier. The results and evaluation of applying these algorithms are presented.

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