Cited 9 times since 2017 (1.1 per year) source: EuropePMC European journal of radiology, Volume 93, 26 4 2017, Pages 1-8 Quantification of aortic annulus in computed tomography angiography: Validation of a fully automatic methodology. Gao X, Boccalini S, Kitslaar PH, Budde RPJ, Attrach M, Tu S, de Graaf MA, Ondrus T, Penicka M, Scholte AJHA, Lelieveldt BPF, Dijkstra J, Reiber JHC
Background
Automatic accurate measuring of the aortic annulus and determination of the optimal angulation of X-ray projection are important for the trans-catheter aortic valve replacement (TAVR) procedure. The objective of this study was to present a novel fully automatic methodology for the quantification of the aortic annulus in computed tomography angiography (CTA) images.
Methods
CTA datasets of 26 patients were analyzed retrospectively with the proposed methodology, which consists of a knowledge-based segmentation of the aortic root and detection of the orientation and size of the aortic annulus. The accuracy of the methodology was determined by comparing the automatically derived results with the reference standard obtained by semi-automatic delineation of the aortic root and manual definition of the annulus plane.
Results
The difference between the automatic annulus diameter and the reference standard by observer 1 was 0.2±1.0mm, with an inter-observer variability of 1.2±0.6mm. The Pearson correlation coefficient for the diameter was good (0.92 for observer 1). For the first time, a fully automatic tool to assess the optimal projection curves was presented and validated. The mean difference between the optimal projection curves calculated based on the automatically defined annulus plane and the reference standard was 6.4° in the cranial/caudal (CRA/CAU) direction. The mean computation time was short with around 60s per dataset.
Conclusion
The new fully automatic and fast methodology described in this manuscript not only provided precise measurements about the aortic annulus size with results comparable to experienced observers, but also predicted optimal X-ray projection curves from CTA images.
