Magnetic Resonance Imaging (MRI) is a safe and widely used diagnostic imaging method that allows in vivo observation of anatomy and characterization of tissues. MRI provides a method to monitor patients without invasive measures, making it suitable for both diagnostics and longitudinal monitoring of various pathologies. A notable example of this is the work carried out by the Alzheimer's Disease Neuroimaging Initiative (ADNI), which utilizes imaging, including multiple MRI techniques, to monitor disease progression in AD patients and evaluates treatment responses and prevention strategies. Similarly, MRI has been extensively used in evaluating diseases in a variety of animal models. In order to detect subtle anatomical changes over time, small differences in MR images must be accurately extracted. Furthermore, to ensure that the extracted differences are due to anatomical changes rather than equipment variance, it becomes essential to monitor and to assess the MRI system stability. In the first chapter of the dissertation, a method for monitoring pre-clinical MRI system performance is discussed. The technique developed during the study provides a fast and simple method to monitor pre-clinical MRI systems but also has applications for all areas of MRI. The second chapter describes the development of a 3D UTE MRI method for pulmonary imaging in freely breathing mice. The development of the 3D UTE sequence for pulmonary MRI has demonstrated its ability to collect images without noticeable motion artifacts and with appreciable signal from the lung parenchyma. Furthermore, images at two distinct respiratory phases were reconstructed from a single data set, providing functional information of the rodents' lungs. Finally, in the third chapter, 3D ¹⁹F UTE MRI is evaluated for imaging in vivo distributions of perfluorocarbon (PFC) nanoemulsions for measuring pulmonary inflammation. Building upon the development of pulmonary imaging, fluorinated contrast agents made from PFCs were used to target immune cells in response to pulmonary pathology. Both 3D ¹H and ¹⁹F UTE MRI were used to acquire pulmonary images of mouse models documented to have pulmonary pathology. Even though the mice had confirmed elevation in alveolar macrophage counts, no visible ¹⁹F signal accumulation within the pulmonary tissue was observed with MRI.

In this thesis, we describe several imaging techniques specifically designed and developed for the assessment of pulmonary structure, function and pathology. We then describe the application of this technology within appropriate biological systems, including the identification, tracking and assessment of lung tumors in a mouse model of lung cancer. The design and development of a Large Image Microscope Array (LIMA), an integrated whole organ serial sectioning and imaging system, is described with emphasis on whole lung tissue. This system provides a means for acquiring 3D pathology of fixed whole lung specimens with no infiltrative embedment medium using a purpose-built vibratome and imaging system. This system enables spatial correspondence between histology and non-invasive imaging modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), providing precise correlation of the underlying 'ground truth' pathology back to the in vivo imaging data. The LIMA system is evaluated using fixed lung specimens from sheep and mice, resulting in large, high-quality pathology datasets that are accurately registered to their respective CT and H&E histology. The implementation of an in vivo micro-CT imaging system in the context of pulmonary imaging is described. Several techniques are initially developed to reduce artifacts commonly associated with commercial micro-CT systems, including geometric gantry calibration, ring artifact reduction and beam hardening correction. A computer controlled Intermittent Iso-pressure Breath Hold (IIBH) ventilation system is then developed for reduction of respiratory motion artifacts in live, breathing mice. A study validating the repeatability of extracting valuable pulmonary metrics using this technique against standard respiratory gating techniques is then presented. The development of an ex vivo laser scanning confocal microscopy (LSCM) and an in vivo catheter based confocal microscopy (CBCM) pulmonary imaging technique is described. Direct high-resolution imaging of sub-pleural alveoli is presented and an alveolar mechanic study is undertaken. Through direct quantitative assessment of alveoli during inflation and deflation, recruitment and de-recruitment of alveoli is quantitatively measured. Based on the empirical data obtained in this study, a new theory on alveolar mechanics is proposed. Finally, a longitudinal mouse lung cancer study utilizing the imaging techniques described and developed throughout this thesis is presented. Lung tumors are identified, tracked and analyzed over a 6-month period using a combination of micro-CT, micro-PET, micro-MRI, LSCM, CBCM, LIMA and H&E histology imaging. The growth rate of individual tumors is measured using the micro-CT data and traced back to the histology using the LIMA system. A significant difference in tumor growth rates within mice is observed, including slow growing, regressive, disappearing and aggressive tumors, while no difference between the phenotype of tumors was found from the H&E histology. Micro-PET and micro-MRI imaging was conducted at the 6-month time point and revealed the limitation of these systems for detection of small lesions ( < 2mm) in this mouse model of lung cancer. The CBCM imaging provided the first high-resolution live pathology of this mouse model of lung cancer and revealed distinct differences between normal, suspicious and tumor regions. In addition, a difference was found between control A/J mice parenchyma and Urethane A/J mice ‘normal’ parenchyma, suggesting a 'field effect' as a result of the Urethane administration and/or tumor burden. In conclusion, a comprehensive murine lung cancer imaging study was undertaken, and new information regarding the progression of tumors over time has been revealed.

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.