Pulmonary emphysema contributes to the chronic airflow limitation characteristic of chronic obstructive pulmonary disease (COPD), which is a leading cause of morbidity and mortality worldwide. Computed tomography (CT) has enabled in vivo assessment of pulmonary emphysema at the macroscopic level, and is commonly used to identify and assess the extent of the disease. During the past decade, the availability of CT imaging data has increased rapidly, while the image quality has continued to improve. High-resolution CT is extremely valuable both for patient diagnosis and for studying diseases at the population level. However, visual assessment of these large data sets is subjective, inefficient, and expensive. This has increased the demand for objective, automatic, and reproducible image analysis methods. For the assessment of pulmonary emphysema on CT, computational models usually aim either to give a measure of the extent of the disease, or to categorize the emphysema subtypes apparent in a scan. The standard methods for quantitating emphysema extent are widely used, but they remain sensitive to changes in imaging protocols and patient inspiration level. For computational subtyping of emphysema, the methods remain at a developmental stage, and one of the main challenges is the lack of reliable label data. Furthermore, the classic emphysema subtypes were defined on autopsy before the availability of CT and could be considered outdated. There is also no consensus on how to match the subtypes on autopsy to the varying emphysema patterns present on CT. This work presents two methodological improvements for analyzing emphysema on CT. For the assessment of emphysema extent, a novel probabilistic approach is introduced and evaluated on a longitudinal data set with varying imaging protocols. The presented model is shown to improve significantly compared to standard methods, particularly at the presence of differing noise levels. The approach is also applied on quantifying emphysema on a large data set of cardiac CT scans, and is shown to improve the prediction of emphysema extent on subsequent full-lung CT scans. The second major contribution of this work applies unsupervised learning to recognizing patterns of emphysema on CT. Instead of trying to reproduce the classic subtypes, the novel approach aims to capture the most dominant variations of lung structure pertaining to emphysema. While removing the reliance on visually assigned labels, the learned patterns are shown to represent different manifestations of emphysema with distinct appearances and regular spatial distributions. The clinical significance of the patterns is also demonstrated, along with high-level performance in the application of content-based image retrieval. The contributions of this work advance the analysis of emphysema on CT by applying novel machine learning approaches to increase the value of the available imaging data. Probabilistic methods improve from the crude standard methods that are currently used to quantitate emphysema, and the value of learning disease patterns directly from image data is demonstrated. The common framework relying on replicating visually assigned labels of outdated subtypes has not achieved widespread acceptance. The methodology presented in this work may have a substantial impact on how emphysema subtypes on CT are recognized and defined in the future.

Thesis (Ph. D.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains vii, 141 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references.

No description available.

Erratum pages inserted inside back cover. / Bibliography: leaves 163-200. / xvi, 215 leaves, [13] leaves of plates : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Measurement of elastin derived peptides may be a powerful tool to evaluate mechanisms of elastin breakdown in vivo and in vitro. In human studies EDP levels may provide an early indicator of subjects undergoing increased elastin degradation that may lead to emphysema, and may serve as a biological marker of the effectiveness of therapeutic antielastases. / Thesis (Ph.D.)--University of Adelaide, Dept. of Pathology, 1994

Erratum pages inserted inside back cover. / Bibliography: leaves 163-200. / xvi, 215 leaves, [13] leaves of plates : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Measurement of elastin derived peptides may be a powerful tool to evaluate mechanisms of elastin breakdown in vivo and in vitro. In human studies EDP levels may provide an early indicator of subjects undergoing increased elastin degradation that may lead to emphysema, and may serve as a biological marker of the effectiveness of therapeutic antielastases. / Thesis (Ph.D.)--University of Adelaide, Dept. of Pathology, 1994

Diss. (sammanfattning) Göteborg : Göteborgs universitet, 2008. / Härtill 4 uppsatser.

Medical imaging is widely used in current healthcare and research settings for various purposes such as diagnosis, treatment options, patient monitoring, longitudinal studies, etc. The two most commonly used imaging modalities in the United States are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Raw images acquired via CT or MRI need to undergo a variety of processing steps prior to being used for the purposes explained above. These processing steps include quality control, noise reduction, anatomical segmentation, tissue classification, etc. However, since medical images often include millions of voxels (smallest 3D units in the image containing information) it is extremely challenging to process them manually by relying on visual inspection and the experience of trained clinicians. In light of this, the field of medical imaging is seeking ways to automate data processing. With the impressive performance of Artificial Intelligence (AI) in the field of Computer Vision, researchers in the medical imaging community have shown increasing interest in utilizing this powerful tool to automate the task of processing medical imaging data. Despite AI’s significant contributions to the medical imaging field, large cohorts of data still remain without optimized and robust AI-based tools to process images efficiently and accurately. This thesis focuses on exploiting large cohorts of CT and MRI data to design AI-based methods for processing medical images using weakly-supervised and supervised learning strategies, as well as mathematical (and/or statistical) modeling and signal processing methods. In particular, we address four image processing problems in this thesis. Namely: 1) We propose a weakly-supervised deep learning method to automate binary quality control of diffusion MRI scans into ‘poor’ and ‘good’ quality classes; 2) We design a weakly-supervised deep learning framework to learn and detect visual patterns related to a set of different artifact categories considered in this work, in order to identify major artifact types present in dMRI volumes; 3) We develop a supervised deep learning method to classify multiple lung texture patterns with association to Emphysema disease on human lung CT scans; 4) We investigate and characterize the properties of two types of negative BOLD response elicited in human brain fMRI scans during visual stimulation using mathematical modeling and signal processing tools. Our results demonstrate that through the use of artificial intelligence and signal processing algorithms: 1) dMRI scans can be automatically categorized into two quality groups (i.e., ‘poor’ vs ‘good’) with a high classification accuracy, enabling rapid sifting of large cohorts of dMRI scans to be utilized in research or clinical settings; 2) Type of the major artifact present in ‘poor’ quality dMRI volumes can be identified robustly and automatically with high precision enabling exclusion/correction of corrupt volumes according to the artifact type contaminating them; 3) Multiple lung texture patterns related to Emphysema disease can be automatically and robustly classified across various large cohorts of CT scans enabling investigation of the disease through longitudinal studies on multiple cohorts; 4) Negative BOLD responses of different categories can be fully characterized on fMRI data collected from visual stimulation of human brain enabling researchers to better understand the human brain functionality through studying cohorts of fMRI scans.

Indiana University-Purdue University Indianapolis (IUPUI) / The complex pathogenesis of emphysema involves disappearance of alveolar structures, in part attributed to alveolar cell apoptosis. The mechanism by which cigarette smoke (CS) induces alveolar cell apoptosis is not known. We hypothesized that ceramides are induced by CS via specific enzymatic pathways that can be manipulated to reduce lung cell apoptosis. CS increased ceramides in the whole lung and in cultured primary structural lung cells. Exposure to CS activated within minutes the acid sphingomyelinase, and within weeks the de novo- ceramide synthesis pathways. Pharmacological inhibition of acid sphingomyelinase significantly attenuated CS-induced apoptosis. To understand the mechanisms by which ceramides induce apoptosis, we investigated the cell types affected and the involvement of RTP801, a CS-induced pro-apoptotic and pro-inflammatory protein. Direct lung augmentation of ceramide caused apoptosis of both endothelial and epithelial type II cells. Ceramide upregulated RTP801 and the transgenic loss of RTP801 inhibited only epithelial, but not endothelial cell apoptosis induced by ceramide. In conclusion, CS induces acid sphingomyelinase-mediated ceramide upregulation and apoptosis in a cell-specific manner, which in epithelial cells involves induction of stress response proteins that may further amplify lung injury. Molecular targeting of amplification pathways may provide therapeutic opportunities to halt emphysema progression.

L’emphysème pulmonaire est, avec la bronchite chronique à laquelle il est généralement associé, une bronchopathie chronique obstructive (BPCO). Ce groupe de maladie a été la sixième cause de mortalité au monde en 1990 et pourrait devenir la troisième en 2020.L’emphysème pulmonaire est défini par un élargissement anormal et permanent des espaces aériens en amont des bronchioles terminales avec destruction des parois alvéolaires sans fibrose évidente. Compte tenu de cette définition, son diagnostic devrait idéalement être basé sur l’histopathologie. Cependant, en pratique clinique, si les EFR sont à la base de la définition de la BPCO, elles ne suffisent pas au diagnostic de l’emphysème pulmonaire.<p><p>La tomodensitométrie (TDM) est une méthode diagnostique d’obtention in vivo de coupes anatomiques qui, formées de milliers de pixels, en font la méthode morphologique la plus précise pour investiguer la structure pulmonaire. Si la juxtaposition de ces pixels – dont la tonalité de gris est fonction de l’atténuation – est à la base de l’image TDM, la même information peut être représentée par la distribution de fréquence de ces atténuations. En présence d’emphysème, la destruction du tissu pulmonaire (et la plus grande proportion d’air) déterminent le déplacement de cette distribution vers les atténuations plus négatives. Plusieurs index TDM dérivés de cette distribution – notamment l’atténuation moyenne, la surface pulmonaire occupée par des valeurs d’atténuation inférieures à un seuil, un percentile particulier de la distribution – sont de possibles mesures de l’étendue de l’emphysème pulmonaire. L’émergence de la technique hélicoïdale, permettant notamment d’explorer tout le parenchyme pulmonaire en une seule apnée, justifie de déterminer les seuils et percentiles adéquats par comparaison à une mesure histologique de référence.<p><p>Au cours de nos études, nous avons montré que les index TDM dérivés de la distribution de fréquence d’atténuation tels que les surfaces relatives de poumon occupées par les coefficients d’atténuation inférieures à -960 UH (RA960) ou -970 UH (RA970) et le premier percentile (p1) sont les index les plus appropriés. En revanche, toujours sur base de comparaisons histo-morphométriques, d’autre index qui reflètent la géométrie des espaces emphysémateux – tels que la distribution de la taille des groupes de pixels adjacents occupés par des coefficients d’atténuation inférieurs à un seuil ou à un percentile – ne sont pas des index valables.<p><p>La dose d’irradiation peut être abaissée à 20 mAs effectifs. Cette réduction est particulièrement appropriée dans une pathologie susceptible de concerner des patients jeunes et l’objet d’examens répétés. Cependant, la dose d’irradiation influençant ces index, elle doit être maintenue constante au cours de suivis longitudinaux.<p><p>En TDM multi-coupes, ces index sont les plus appropriés quelque soit l’épaisseur des coupes. Cependant, cette épaisseur influençant ces index, elle doit aussi être maintenue constante au cours de suivis longitudinaux.<p><p>L’inspiration incomplète induit une sous-estimation statistiquement significative mais cliniquement insignifiante de l’étendue de l’emphysème pulmonaire. La destruction du tissu pulmonaire et l’hyperinflation ont des influences séparées sur les index TDM, faisant recommander leur ajustement aux valeurs prédites de la CPT.<p><p> / Doctorat en Sciences médicales / info:eu-repo/semantics/nonPublished

Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory disease characterized by airway inflammation and abnormal alveolar enlargement. It is the third leading cause of death around the world. Although extensive research efforts have been made, there is still no curable treatment available for lung tissue damage in patients with COPD. Therefore, it is of great significance to elucidate the mechanisms of tissue damage and repair in COPD. As the first barrier against environmental insults and pathogens, pulmonary epithelial cells play an essential role in regulating injury response and repair. However, how pulmonary epithelial cells contribute to irreversible alveolar destruction in COPD is not well understood. In this study, we elucidated the mechanisms of epithelial cell damage in both cigarette smoke-induced COPD and alpha1 antitrypsin deficiency (AATD)-associated genetic COPD. To investigate alveolar epithelial cell damage and repair in cigarette smoke-induced emphysema, a lineage tracing model was utilized to fluorescently label and chase alveolar type II (AT2) epithelial cells, the adult progenitor cells in the alveolar epithelium. An assessment of cigarette smoke-induced changes in cellular composition and regenerative capacity of the alveolar epithelial cells was performed. Cigarette smoke was found to impede the AT2-directed alveolar epithelial regeneration and repair process, and this impaired progenitor cell function was not restored after smoke cessation. Moreover, comparison analysis between stains that are sensitive and resistant to smoke-induced damage revealed that deficiency in lipid metabolism may contribute to the dysregulation of alveolar epithelial repair by AT2 cells. Restoring alveolar progenitor functions through lipid metabolism may serve as a novel therapeutic for alveolar destruction in smoke-induced COPD. To explore the mechanism of epithelial damage in AATD-associated genetic COPD, we utilized a PiZ (p.Glu342Lys) transgenic mouse model expressing human ZAAT protein. Morphometric analysis of PiZ lungs suggests that the accumulation of ZAAT polymers in the lung directly leads to the spontaneous development of emphysema. To investigate epithelial damage induced by zAAT accumulation, we isolated the epithelial cell population from the lung of PiZ mice. We identified epithelial-specific expression of cleaved caspase 3, indicating a direct cytotoxic effect of ZAAT in impairing epithelial function and inducing epithelial cell death. Future therapeutics could directly target the cytotoxicity of pulmonary epithelial cells in AATD to reduce lung tissue damage. Overall, our findings suggest that pulmonary epithelial damage plays an essential role in the pathogenesis of lung tissue damage in COPD. Future epithelial cell-based therapies may contribute to pulmonary re-epithelialization and tissue repair in both cigarette smoke-induced and AATD-associated COPD.