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Symmetry, outliers, and geodesics in coronary artery centerline reconstruction from rotational angiography
Author(s) -
Unberath Mathias,
Taubmann Oliver,
Hell Michaela,
Achenbach Stephan,
Maier Andreas
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12512
Subject(s) - imaging phantom , reprojection error , point cloud , geodesic , ground truth , computer vision , artificial intelligence , iterative reconstruction , epipolar geometry , projection (relational algebra) , outlier , algorithm , mathematics , computer science , geometry , nuclear medicine , medicine , image (mathematics)
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.