Optimal C-arm angulation during transcatheter aortic valve replacement: Accuracy of a rotational C-arm computed tomography based three dimensional heart model.

AIM: To investigate the accuracy of a rotational C-arm CT-based 3D heart model to predict an optimal C-arm configuration during transcatheter aortic valve replacement (TAVR). METHODS: Rotational C-arm CT (RCT) under rapid ventricular pacing was performed in 57 consecutive patients with severe aortic stenosis as part of the pre-procedural cardiac catheterization. With prototype software each RCT data set was segmented using a 3D heart model. From that the line of perpendicularity curve was obtained that generates a perpendicular view of the aortic annulus according to the right-cusp rule. To evaluate the accuracy of a model-based overlay we compared model- and expert-derived aortic root diameters. RESULTS: For all 57 patients in the RCT cohort diameter measurements were obtained from two independent operators and were compared to the model-based measurements. The inter-observer variability was measured to be in the range of 0°-12.96° of angular C-arm displacement for two independent operators. The model-to-operator agreement was 0°-13.82°. The model-based and expert measurements of aortic root diameters evaluated at the aortic annulus (r = 0.79, P < 0.01), the aortic sinus (r = 0.93, P < 0.01) and the sino-tubular junction (r = 0.92, P < 0.01) correlated on a high level and the Bland-Altman analysis showed good agreement. The interobserver measurements did not show a significant bias. CONCLUSION: Automatic segmentation of the aortic root using an anatomical model can accurately predict an optimal C-arm configuration, potentially simplifying current clinical workflows before and during TAVR.

Left ventricular contrast injection with rotational C-arm CT improves accuracy of aortic annulus measurement during cardiac catheterisation.

AIMS: Introduction of a novel contrast injection protocol during rotational C-arm CT (RCT) in cardiac catheterisation of patients with aortic stenosis for aortic root assessment. METHODS AND RESULTS: Fifty-two patients underwent RCT imaging with contrast injection performed either into the aorta (Ao-RCT, n=25) or into the left ventricle (LV-RCT, n=27). Aortic annulus diameters were assessed in a multiplanar reconstruction view and compared with corresponding multidetector computed tomography (MDCT). LV contrast injection additionally enabled measurement of the left ventricular outflow tract (LVOT). LV-RCT improved the accuracy of annulus measurements and correlated well with MDCT data in comparison with Ao-RCT and MDCT (r=0.91, r=0.76, respectively). The Bland-Altman analysis showed smaller differences in MDCT and LV-RCT annulus measurements than between MDCT and Ao-RCT (LV-RCT: mean=0.4 mm, limits of agreement -1.5-2.3 mm vs. Ao-RCT: mean=0.1 mm, limits of agreement -3.4-3.6 mm). The inter-observer agreement for the annulus measurements was significantly increased for LV-RCT as calculated by the intra-class coefficient (ICC=0.85) in comparison with Ao-RCT (ICC=0.52). CONCLUSIONS: Cardiac catheterisation including LV-RCT offers complementary assessment of left ventricular function, aortic valve anatomy, coronary angiography and arterial access routes. LV-RCT for aortic root measurements shows better correlation to MDCT than standard Ao-RCT protocols.

Optimizing GHT-based heart localization in an automatic segmentation chain.

With automated image analysis tools entering rapidly the clinical practice, the demands regarding reliability, accuracy, and speed are strongly increasing. Systematic testing approaches to determine optimal parameter settings and to select algorithm design variants become essential in this context. We present an approach to optimize organ localization in a complex segmentation chain consisting of organ localization, parametric organ model adaptation, and deformable adaptation. In particular, we consider the Generalized Hough Transformation (GHT) and 3D heart segmentation in Computed Tomography Angiography (CTA) images. We rate the performance of our GHT variant by the initialization error and by computation time. Systematic parameter testing on a compute cluster allows to identify a parametrization with a good tradeoff between reliability and speed. This is achieved with coarse image sampling, a coarse Hough space resolution and a filtering step that we introduced to remove unspecific edges. Finally we show that optimization of the GHT parametrization results in a segmentation chain with reduced failure rates.