Automatic aortic root landmark detection in CTA images for preprocedural planning of transcatheter aortic valve implantation

Abstract

Transcatheter aortic valve implantation is currently a well-established minimal invasive treatment option for patients with severe aortic valve stenosis. CT Angiography is used for the pre-operative planning and sizing of the prosthesis. To reduce the inconsistency in sizing due to interobserver variability, we introduce and evaluate an automatic aortic root landmarks detection method to determine the sizing parameters. The proposed algorithm detects the sinotubular junction, two coronary ostia, and three valvular hinge points on a segmented aortic root surface. Using these aortic root landmarks, the automated method determines annulus radius, annulus orientation, and distance from annulus plane to right and left coronary ostia. Validation is performed by the comparison with manual measurements of two observers for 40 CTA image datasets. Detection of landmarks showed high accuracy where the mean distance between the automatically detected and reference landmarks was 2.81 ± 2.08 mm, comparable to the interobserver variation of 2.67 ± 2.52 mm. The mean annulus to coronary ostium distance was 16.9 ± 3.3 and 17.1 ± 3.3 mm for the automated and the reference manual measurements, respectively, with a mean paired difference of 1.89 ± 1.71 mm and interobserver mean paired difference of 1.38 ± 1.52 mm. Automated detection of aortic root landmarks enables automated sizing with good agreement with manual measurements, which suggests applicability of the presented method in current clinical practice.

Automatic aortic root segmentation and anatomical landmarks detection for TAVI procedure planning

“…ter-observer variability discussed in the work of Zhao et al [4] and Elattar et al [26], a TAVI sizing can be considered accurate enough with results below 3mm, which is the case in this study.…”

Automatic aortic valve landmark localization in coronary CT angiography using colonial walk

“…The user specifies the initial point, and manually marked the hinges and the ostia. Most recently, Elattar et al [ 20 ] introduced an automatic method, where the aortic hinges and coronary ostia are detected on the surface of the aortic root. However, the surface of the root is obtained after segmentation based on thresholding followed by connected component analysis, which has robustness issue under image noise.…”

“…The proposed method showed a high localization accuracy in highly calcified preoperative CT volumes of TAVI patients. The proposed method also ensures high computational efficiency compared to previous landmark localization methods [ 17 , 20 , 22 ] because, in the voxel-wise classification framework, we need to test all of the voxels inside the considered volume, while the colonial walk only needs to traverse the tree for the voxels its walkers step into. Random walks are found to be implemented on labeling the pixels in multilabel, interactive image segmentation [ 37 ].…”

Minimal invasive aortic valve replacement: associations of radiological assessments with procedure complexity

“…The calcium volume is calculated after setting a volume of interest including aortic annulus and leaflet. Calcifications in the left ventricular outflow tract, coronary arteries and ascending aorta are excluded [23].…”