Ventilation imaging using 4D-CT is a convenient and cost effective functional imaging methodology which might be of value in radiotherapy treatment planning to spare functional lung volumes. To calculate ventilation imaging from 4D-CT we must use deformable image registration (DIR). This study validates the DIR methods and investigates the dependence of calculated ventilation on DIR methods and ventilation algorithms. The first hypothesis is if ventilation algorithms are robust then they will be insensitive to the precise DIR used provided the DIR is accurate. The second hypothesis is that the change in Houndsfield Unit (HU) method is less dependent on the DIR used and depends more on the CT image quality due to the inherent noise of HUs in normal CT imaging. DIR of the normal end expiration and inspiration phases of the 4D-CT images was used to correlate the voxels between the two respiratory phases. All DIR algorithms were validated using a 4D pixel-based and point-validated breathing thorax model, consisting of a 4D-CT image data set along with associated landmarks. Three different DIR algorithms, Optical Flow (OF), Diffeomorphic Demons (DD) and Diffeomorphic Morphons (DM), were retrospectively applied to the same group of 10 esophagus and 10 lung cancer cases all of which had associated 4D-CT image sets that encompassed the entire lung volume. Three different ventilation calculation algorithms were compared (Jacobian, ΔV, and HU) using the Dice similarity coefficient comparison. In the validation of the DIR algorithms, the average target registration errors with one standard deviation for the DIR algorithms were 1.6 ± 0.7 mm, maximum 3.1 mm for OF, 1.3 ± 0.6 mm, maximum 3.3 mm for DM, 1.3 ± 0.6 mm, maximum 2.8 mm for DD, indicating registration errors were within 2 voxels. Dependence of ventilation images on the DIR was greater for the ΔV and the Jacobian methods than for the HU method. The Dice similarity coefficient for 20% of low ventilation volume for ΔV was 0.33 ± 0.03 between OF and DM, 0.44 ± 0.05 between OF and DD and 0.51 ± 0.04 between DM and DD. The similarity comparisons for Jacobian was 0.32 ± 0.03, 0.44 ± 0.05 and 0.51 ± 0.04 respectively, and for HU 0.53 ± 0.03, 0.56 ± 0.03 and 0.76 ± 0.04 respectively. Dependence of ventilation images on the ventilation method used showed good agreement between the ΔV and Jacobian methods but differences between these two and the HU method were significantly greater. Dice similarity coefficient for using OF as DIR was 0.86 ± 0.01 between ΔV and Jacobian, 0.28 ± 0.04 between ΔV and HU and 0.28 ± 0.04 between Jacobian and HU respectively. When using DM or DD as DIR, similar values were obtained when comparing the different ventilation calculation methods. The similarity values for 20% of the high ventilation volume were close to those found for the 20% low ventilation volume. Mean target registration error for all three DIR methods was within one voxel suggesting that the registration done by either of the methods is quite accurate. Ventilation calculation from 4D-CT demonstrates some degree of dependency on the DIR algorithm employed. Similarities between ΔV and Jacobian are higher than between ΔV and HU and Jacobian and HU. This shows that ΔV and Jacobian are very similar, but HU is a very different ventilation calculation method.

<p>The ability to image ventilation and perfusion enables pulmonary researchers to study functional metrics of gas exchange on a regional basis. There is a huge interest in applying imaging methods to study the large number of genetic models of pulmonary diseases available in small animals. Existing techniques to image ventilation and perfusion are often associated with low spatial resolution and ionizing radiation. Magnetic Resonance Imaging (MRI) has been demonstrated successfully for ventilation and perfusion studies in humans. Translating these techniques in small animals remains challenging. This work addresses the ventilation and perfusion imaging in small animals using MRI. </p><p>Qualitative ventilation imaging in rats and mice is possible and has been demonstrated using MRI, however perfusion imaging remains a challenge. In humans and large animals perfusion can be assessed using dynamic contrast-enhanced (DCE) MRI with a single bolus injection of a gadolinium (Gd)-based contrast agent. But the method developed for the clinic cannot be translated directly to image the rat due to the combined requirements of higher spatial and temporal resolution. This work describes a novel image acquisition technique staggered over multiple, repeatable bolus injections of contrast agent using an automated microinjector, synchronized with image acquisition to achieve dynamic first-pass contrast enhancement in the rat lung. This allows dynamic first-pass imaging that can be used to quantify pulmonary perfusion. Further improvements are made in the spatial and temporal resolution by combining the multiple injection acquisition method with Interleaved Radial Imaging and 'Sliding window-keyhole' reconstruction (IRIS). The results demonstrate a simultaneous increase in spatial resolution (<200>um) and temporal resolution (<200>ms) over previous methods, with a limited loss in signal-to-noise-ratio. </p><p>While is it possible to create high resolution images of ventilation in rats using hyperpolarized ³He, extracting meaningful quantitative information indicative of changes in ventilation is difficult. In this work, we also present a signal calibration technique used to normalize the signal of ³He to volume of ³He which can then be used to extract quantitative information of changes in ventilation via normalized difference maps. Combining the techniques for quantitative ventilation and quantitative perfusion we perform studies of change in ventilation/perfusion (V/Q) before and after airway obstruction in rats. The technique is sensitive in detecting statistically significant differences in the heterogeneity of the distribution of V/Q ratio.</p> / Dissertation