Anatomical and functional lung imaging with MRI
Autor: | Tibiletti, Marta |
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Přispěvatelé: | Rasche, Volker, Nagel, Armin |
Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
Tiermodell
Perfusion quantification Kernspintomografie Ventilation quantification Obstruktive Ventilationsstörung Magnetic resonance imaging Lung diseases Diagnosis Self-gating Zero echo time Models Animal Ultra short echo time Pulmonary disease Chronic obstructive ddc:610 Lung parenchyma visualization Methods DDC 610 / Medicine & health |
Popis: | The non-invasive quantifiation of pathogenic changes in animal models of diseases is an essential component of the longitudinal analysis of the progression / regression of the condition with pharmacological therapy. By means of Magnetic Resonance Imaging (MRI) a plurality of anatomical and functional parameters can be detected. The application of MRI to the longitudinal recording of the progression / regression of certain diseases is still limited by difficulties in achieving accurate quantification and sufficient reproducibility of results in small animals. Indeed, in comparison to clinical imaging, in vivo imaging in small animals is more challenging, due to the requirements for high spatial and temporal resolution. This work focuses on lung imaging in rodents, aiming at the visualization of anatomical characteristics and the extraction of functional parameters in healthy animals and in models of lung disease. While MRI provides excellent soft tissue contrast in most tissues, lung imaging with MRI is relatively less developed. The main reasons for this are : - Lung tissue density is lower compared with most other tissues, decreasing signifi cantly the MR signal arising from it. - The high number of tissue-air interfaces causes rapid signal decay and conventional MRI acquisition methods are not able to detect it. This effect is more pronounced at the high or ultra high magnetic field strengths commonly used for small animal imaging. -Cardiorespiratory motion must be adequately compensated to obtain good image quality. This thesis focuses on the application of non-conventional MRI acquisition methods, Ultra short Echo Time (UTE) and Zero Echo Time (ZTE), which are suited for the detection of lung signal and are robust to movement artifacts. Particular attention is given to the implementation and comparison of different methods for retrospective respiratory gating in UTE acquisitions. This allows for the consideration of respiratorymovement during free breathing acquisitions, yielding an improved image quality and enabling the reconstruction of different respiratory positions from a single continuousacquisition. Beyond anatomical lung characterization, this work explores MRI-based techniques aiming for the local quantification of pulmonary functional parameters. The main function of the lungs is allowing for gas exchange between blood and inhaled air. The local quantification of ventilation and tissue perfusion is pivotal to the evaluation of the lung function. Yet, a limited amount of research has been devoted to the implementation of MRI methods for the reliable and repeatable quantification of such parameters. In order to guarantee the wide applicability of its results, this thesis focuses on protocols which do not require specialized hardware, expensive equipment or exceedingly long acquisition times. The experiments described in this work were evaluated both on healthy and diseased rats. In particular, a model of emphysema was investigated. Emphysema is characterized by abnormal permanent enlargement of air spaces, accompanied by tissue destruction. It is categorized as one of the forms of Chronic Obstructive Pulmonary disease (COPD), which is typically caused by smoking in human subjects. A similar tissue damage can be created in the lungs of small animals instilling pancreatic elastase, an enzyme able to destroy connective tissue. In this work, it was shown with MRI that such treatment results in a decrease in lung parenchyma density and the lack of proper ventilation in the tissue. |
Databáze: | OpenAIRE |
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