13 March 2018 Geometric modeling of the aortic inner and outer vessel wall from CTA for aortic dissection analysis
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Abstract
In this paper, we present a novel method for modeling both layers of the aortic walls in cases of aortic dissections for analysis from Computed Tomography Angiography. It involves a fast initialization of the associated physiological and pathological lumina and further editing on non-linearly formatted and cross-sectional views. Fast and accurate derivation of 3D models of these inner and outer vessel walls is crucial to analyze true and false lumen, to accelerate processing times in research studies, and to answer therapy questions. Since the aorta is a relatively large vessel, our system makes use of a point-based surface interpolation with compactly supported radial basis functions requiring only few surface constraints. Where possible, we use a semi-automatic approach to segment the vessel walls using an Active Contour Model, which detects the contours in the vessel’s cross-sectional planes, stating the constraints for interpolation. After initialization, editing on non-linearly formatted and crosssectional views is possible due to handling user input through tangent frame bundles to dismiss contradictory surface samples before updating the models with the new constraints. Our proposed method was evaluated in a user study to measure processing times and achievable model accuracy with respect to an expert-defined ground truth. The users needed 19 minutes on average to derive one model (both walls) and attained a mean surface distance of about 1.0 mm for the outer vessel wall, respectively, 1.6 mm for the inner wall. Using our method instead of open source program for geometric modeling saves 26 minutes per dataset.
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Katharina Eigen, Michael Wels, Daniel-Sebastian Dohle, Michael Suehling, Andreas Maier, "Geometric modeling of the aortic inner and outer vessel wall from CTA for aortic dissection analysis", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 105761X (13 March 2018); doi: 10.1117/12.2293096; https://doi.org/10.1117/12.2293096
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