Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images

We present a pulmonary vessel segmentation algorithm, which is fast, fully automatic and robust. It uses a coarse segmentation of the airway tree and a left and right lung labeled volume to restrict a vessel enhancement filter, based on an offset medialness function, to the lungs. We show the application of our algorithm on contrast-enhanced CT images, where we derive a clinical parameter to detect pulmonary hypertension (PH) in patients. Results on a dataset of 24 patients show that quantitative indices derived from the segmentation are applicable to distinguish patients with and without PH. Further work-in-progress results are shown on the VESSEL12 challenge dataset, which is composed of non-contrast-enhanced scans, where we range in the midfield of participating contestants.Comment: Part of the OAGM/AAPR 2013 proceedings (1304.1876

Automation Process for Morphometric Analysis of Volumetric CT Data from Pulmonary Vasculature in Rats

With advances in medical imaging scanners, it has become commonplace to generate large multidimensional datasets. These datasets require tools for a rapid, thorough analysis. To address this need, we have developed an automated algorithm for morphometric analysis incorporating A Visualization Workshop computational and image processing libraries for three-dimensional segmentation, vascular tree generation and structural hierarchical ordering with a two-stage numeric optimization procedure for estimating vessel diameters. We combine this new technique with our mathematical models of pulmonary vascular morphology to quantify structural and functional attributes of lung arterial trees. Our physiological studies require repeated measurements of vascular structure to determine differences in vessel biomechanical properties between animal models of pulmonary disease. Automation provides many advantages including significantly improved speed and minimized operator interaction and biasing. The results are validated by comparison with previously published rat pulmonary arterial micro-CT data analysis techniques, in which vessels were manually mapped and measured using intense operator intervention

Quantification of Pulmonary Arterial Wall Distensibility Using Parameters Extracted from Volumetric Micro-CT Images

Stiffening, or loss of distensibility, of arterial vessel walls is among the manifestations of a number of vascular diseases including pulmonary arterial hypertension. We are attempting to quantify the mechanical properties of vessel walls of the pulmonary arterial tree using parameters derived from high-resolution volumetric x-ray CT images of rat lungs. The pulmonary arterial trees of the excised lungs are filled with a contrast agent. The lungs are imaged with arterial pressures spanning the physiological range. Vessel segment diameters are measured from the inlet to the periphery, and distensibilities calculated from diameters as a function of pressure. The method shows promise as an adjunct to other morphometric techniques such as histology and corrosion casting. It possesses the advantages of being nondestructive, characterizing the vascular structures while the lungs are imaged rapidly and in a near-physiological state, and providing the ability to associate mechanical properties with vessel location in the intact tree hierarchy

Semiautomated Skeletonization of the Pulmonary Arterial Tree in Micro-CT Images

We present a simple and robust approach that utilizes planar images at different angular rotations combined with unfiltered back-projection to locate the central axes of the pulmonary arterial tree. Three-dimensional points are selected interactively by the user. The computer calculates a sub- volume unfiltered back-projection orthogonal to the vector connecting the two points and centered on the first point. Because more x-rays are absorbed at the thickest portion of the vessel, in the unfiltered back-projection, the darkest pixel is assumed to be the center of the vessel. The computer replaces this point with the newly computer-calculated point. A second back-projection is calculated around the original point orthogonal to a vector connecting the newly-calculated first point and user-determined second point. The darkest pixel within the reconstruction is determined. The computer then replaces the second point with the XYZ coordinates of the darkest pixel within this second reconstruction. Following a vector based on a moving average of previously determined 3- dimensional points along the vessel\u27s axis, the computer continues this skeletonization process until stopped by the user. The computer estimates the vessel diameter along the set of previously determined points using a method similar to the full width-half max algorithm. On all subsequent vessels, the process works the same way except that at each point, distances between the current point and all previously determined points along different vessels are determined. If the difference is less than the previously estimated diameter, the vessels are assumed to branch. This user/computer interaction continues until the vascular tree has been skeletonized

Micro-CT Image-Derived Metrics Quantify Arterial Wall Distensibility Reduction in a Rat Model of Pulmonary Hypertension

We developed methods to quantify arterial structural and mechanical properties in excised rat lungs and applied them to investigate the distensibility decrease accompanying chronic hypoxia-induced pulmonary hypertension. Lungs of control and hypertensive (three weeks 11% O2) animals were excised and a contrast agent introduced before micro-CT imaging with a special purpose scanner. For each lung, four 3D image data sets were obtained, each at a different intra-arterial contrast agent pressure. Vessel segment diameters and lengths were measured at all levels in the arterial tree hierarchy, and these data used to generate features sensitive to distensibility changes. Results indicate that measurements obtained from 3D micro-CT images can be used to quantify vessel biomechanical properties in this rat model of pulmonary hypertension and that distensibility is reduced by exposure to chronic hypoxia. Mechanical properties can be assessed in a localized fashion and quantified in a spatially-resolved way or as a single parameter describing the tree as a whole. Micro-CT is a nondestructive way to rapidly assess structural and mechanical properties of arteries in small animal organs maintained in a physiological state. Quantitative features measured by this method may provide valuable insights into the mechanisms causing the elevated pressures in pulmonary hypertension of differing etiologies and should become increasingly valuable tools in the study of complex phenotypes in small-animal models of important diseases such as hypertension

Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135074/1/mp4558.pd

Non-uniform central airways ventilation model based on vascular segmentation

Improvements in the understanding of the physiology of the central airways require an appropriate representation of the non-uniform ventilation at its terminal branches. This paper proposes a new technique for estimating the non-uniform ventilation at the terminal branches by modelling the volume change of their distal peripheral airways, based on vascular segmentation. The vascular tree is used for sectioning the dynamic CT-based 3D volume of the lung at 11 time points over the breathing cycle of a research animal. Based on the mechanical coupling between the vascular tree and the remaining lung tissues, the volume change of each individual lung segment over the breathing cycle was used to estimate the non-uniform ventilation of its associated terminal branch. The 3D lung sectioning technique was validated on an airway cast model of the same animal pruned to represent the truncated dynamic CT based airway geometry. The results showed that the 3D lung sectioning technique was able to estimate the volume of the missing peripheral airways within a tolerance of 2%. In addition, the time-varying non-uniform ventilation distribution predicted by the proposed sectioning technique was validated against CT measurements of lobar ventilation and showed good agreement. This significant modelling advance can be used to estimate subject-specific non-uniform boundary conditions to obtain subject-specific numerical models of the central airway flow