Cardiac computed tomography (CT) is a valuable tool for functional mechanical heart valve (MHV) assessment. An important aspect of bileaflet MHV assessment is evaluation and measurement of leaflet opening and closing angles. Performed manually however, it is a laborious and time consuming task. In this paper, we propose an automated approach for bileaflet MHV leaflet angle computation. This method consists of four steps. After a one click selection of the MHV region on an axial image, an automatic MHV extraction using thresholding and connected component analysis based on voxel intensities is performed. Then the MHV component (valve ring and two leaflets) positions are identified using random sample consensus and least square fitting. Finally, the angles are automatically computed based on the orientation of the components in each timeframe. Five multiphase CT scans from patients with a bileaflet MHV containing between 14 and 17 timepoints were used for development and another fifteen were used for evaluation. The detected MHV components were scored for their overlap with real components as successful or unsuccessful. For successful results, the angles were compared to those measured by a radiologist. Qualitatively evaluated on a dataset of 222 images, a total of 398 out of 444 angle computations (89.6%) were rated as successful. Compared to the angles measured by the radiologist, the successful angles showed a mean difference of 0.54° ± 3.63° from the manual calculations. The method provides a high success rate and an accurate computation of leaflet opening angles compared to manual measurements.

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Keywords Computer-aided detection and diagnosis, Heart, Pattern recognition and classification, Quantification and estimation, Shape analysis, X-ray imaging and computed tomography
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Journal IEEE Transactions on Medical Imaging
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Androulakis, I. (Ioannis), Faure, M.E. (Marguerite E.), Budde, R.J. (Ricardo J.P.), & van Walsum, T.W. (2018). Automated quantification of bileaflet mechanical heart valve leaflet angles in CT images. IEEE Transactions on Medical Imaging. doi:10.1109/TMI.2018.2871366