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Title: Developing clinical measures of lung function in COPD patients using medical imaging and computational modelling
Author: Doel, Thomas MacArthur Winter
ISNI:       0000 0004 2745 9129
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2012
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Chronic obstructive pulmonary disease (COPD) describes a range of lung conditions including emphysema, chronic bronchitis and small airways disease. While COPD is a major cause of death and debilitating illness, current clinical assessment methods are inadequate: they are a poor predictor of patient outcome and insensitive to mild disease. A new imaging technology, hyperpolarised xenon MRI, offers the hope of improved diagnostic techniques, based on regional measurements using functional imaging. There is a need for quantitative analysis techniques to assist in the interpretation of these images. The aim of this work is to develop these techniques as part of a clinical trial into hyperpolarised xenon MRI. In this thesis we develop a fully automated pipeline for deriving regional measurements of lung function, making use of the multiple imaging modalities available from the trial. The core of our pipeline is a novel method for automatically segmenting the pulmonary lobes from CT data. This method combines a Hessian-based filter for detecting pulmonary fissures with anatomical cues from segmented lungs, airways and pulmonary vessels. The pipeline also includes methods for segmenting the lungs from CT and MRI data, and the airways from CT data. We apply this lobar map to the xenon MRI data using a multi-modal image registration technique based on automatically segmented lung boundaries, using proton MRI as an intermediate stage. We demonstrate our pipeline by deriving lobar measurements of ventilated volumes and diffusion from hyperpolarised xenon MRI data. In future work, we will use the trial data to further validate the pipeline and investigate the potential of xenon MRI in the clinical assessment of COPD. We also demonstrate how our work can be extended to build personalised computational models of the lung, which can be used to gain insights into the mechanisms of lung disease.
Supervisor: Gavaghan, David J.; Grau, Vicente Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Respiratory medicine ; Medical Engineering ; Mathematical modeling (engineering) ; Mathematical biology ; Medical Sciences ; Biomedical engineering ; Image understanding ; medical imaging ; medical image analysis ; pulmonary imaging ; respiratory imaging ; respiratory disease ; lung disease ; computational modelling ; biomedical imaging ; magnetic resonance imaging ; computed tomography ; chronic obstructive pulmonary disease ; hyperpolarised gas MRI ; hyperpolarised xenon MRI