Quantitative 3D reconstruction of airway and pulmonary vascular trees using HRCT

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Abstract
Accurate quantitative measurements of airway and vascular dimensions are essential to evaluate function in the normal and diseased lung. In this report, a novel method is described for three-dimensional extraction and analysis of pulmonary tree structures using data from High Resolution Computed Tomography (HRCT). Serially scanned two-dimensional slices of the lower left lobe of isolated dog lungs were stacked to create a volume of data. Airway and vascular trees were three-dimensionally extracted using a three dimensional seeded region growing algorithm based on difference in CT number between wall and lumen. To obtain quantitative data, we reduced each tree to its central axis. From the central axis, branch length is measured as the distance between two successive branch points, branch angle is measured as the angle produced by two daughter branches, and cross sectional area is measured from a plane perpendicular to the central axis point. Data derived from these methods can be used to localize and quantify structural differences both during changing physiologic conditions and in pathologic lungs.

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