Mathias Unberath1,2, Oliver Taubmann1,2, Michaela Hell3, Stephan Achenbach3, Andreas Maier1. 1. Department of Computer Science, Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany. 2. Erlangen Graduate School in Advanced Optical Technologies (SAOT), Erlangen, Germany. 3. Department of Cardiology, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany.
Abstract
PURPOSE: The performance of many state-of-the-art coronary artery centerline reconstruction algorithms in rotational angiography heavily depends on accurate two-dimensional centerline information that, in practice, is not available due to segmentation errors. To alleviate the need for correct segmentations, we propose generic extensions to symbolic centerline reconstruction algorithms that target symmetrization, outlier rejection, and topology recovery on asymmetrically reconstructed point clouds. METHODS: Epipolar geometry- and graph cut-based reconstruction algorithms are used to reconstruct three-dimensional point clouds from centerlines in reference views. These clouds are input to the proposed methods that consist of (a) merging of asymmetric reconstructions, (b) removal of inconsistent three-dimensional points using the reprojection error, and (c) projection domain-informed geodesic computation. We validate our extensions in a numerical phantom study and on two clinical datasets. RESULTS: In the phantom study, the overlap measure between the reconstructed point clouds and the three-dimensional ground truth increased from 68.4 ± 9.6% to 85.9 ± 3.3% when the proposed extensions were applied. In addition, the averaged mean and maximum reprojection error decreased from 4.32 ± 3.03 mm to 0.189 ± 0.182 mm and from 8.39 ± 6.08 mm to 0.392 ± 0.434 mm. For the clinical data, the mean and maximum reprojection error improved from 1.73 ± 0.97 mm to 0.882 ± 0.428 mm and from 3.83 ± 1.87 mm to 1.48 ± 0.61 mm, respectively. CONCLUSIONS: The application of the proposed extensions yielded superior reconstruction quality in all cases and effectively removed erroneously reconstructed points. Future work will investigate possibilities to integrate parts of the proposed extensions directly into reconstruction.
PURPOSE: The performance of many state-of-the-art coronary artery centerline reconstruction algorithms in rotational angiography heavily depends on accurate two-dimensional centerline information that, in practice, is not available due to segmentation errors. To alleviate the need for correct segmentations, we propose generic extensions to symbolic centerline reconstruction algorithms that target symmetrization, outlier rejection, and topology recovery on asymmetrically reconstructed point clouds. METHODS: Epipolar geometry- and graph cut-based reconstruction algorithms are used to reconstruct three-dimensional point clouds from centerlines in reference views. These clouds are input to the proposed methods that consist of (a) merging of asymmetric reconstructions, (b) removal of inconsistent three-dimensional points using the reprojection error, and (c) projection domain-informed geodesic computation. We validate our extensions in a numerical phantom study and on two clinical datasets. RESULTS: In the phantom study, the overlap measure between the reconstructed point clouds and the three-dimensional ground truth increased from 68.4 ± 9.6% to 85.9 ± 3.3% when the proposed extensions were applied. In addition, the averaged mean and maximum reprojection error decreased from 4.32 ± 3.03 mm to 0.189 ± 0.182 mm and from 8.39 ± 6.08 mm to 0.392 ± 0.434 mm. For the clinical data, the mean and maximum reprojection error improved from 1.73 ± 0.97 mm to 0.882 ± 0.428 mm and from 3.83 ± 1.87 mm to 1.48 ± 0.61 mm, respectively. CONCLUSIONS: The application of the proposed extensions yielded superior reconstruction quality in all cases and effectively removed erroneously reconstructed points. Future work will investigate possibilities to integrate parts of the proposed extensions directly into reconstruction.
Authors: Alexander Preuhs; Martin Berger; Sebastian Bauer; Thomas Redel; Mathias Unberath; Stephan Achenbach; Andreas Maier Journal: Int J Comput Assist Radiol Surg Date: 2018-06-01 Impact factor: 2.924