Pulmonary vasculature in dogs assessed by three-dimensional fractal analysis and chemometrics
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Fractal analysis of canine pulmonary vessels could allow quantification of their space-filling properties. Aims of this prospective, analytical, cross-sectional study were to describe methods for reconstructing three dimensional pulmonary arterial vascular trees from computed tomographic pulmonary angiogram, applying fractal analyses of these vascular trees in dogs with and without diseases that are known to predispose to thromboembolism, and testing the hypothesis that diseased dogs would have a different fractal dimension than healthy dogs. A total of 34 dogs were sampled. Based on computed tomographic pulmonary angiograms findings, dogs were divided in three groups: diseased with pulmonary thromboembolism (n = 7), diseased but without pulmonary thromboembolism (n = 21), and healthy (n = 6). An observer who was aware of group status created three-dimensional pulmonary artery vascular trees for each dog using a semiautomated segmentation technique. Vascular three-dimensional reconstructions were then evaluated using fractal analysis. Fractal dimensions were analyzed, by group, using analysis of variance and principal component analysis. Fractal dimensions were significantly different among the three groups taken together (P = 0.001), but not between the diseased dogs alone (P = 0.203). The principal component analysis showed a tendency of separation between healthy control and diseased groups, but not between groups of dogs with and without pulmonary thromboembolism. Findings indicated that computed tomographic pulmonary angiogram images can be used to reconstruct three-dimensional pulmonary arterial vascular trees in dogs and that fractal analysis of these three-dimensional vascular trees is a feasible method for quantifying the spatial relationships of pulmonary arteries. These methods could be applied in further research studies on pulmonary and vascular diseases in dogs.
|Journal||Veterinary Radiology & Ultrasound|
|Publication status||Published - 2017|
- Journal Article