Automatic Detection of the Aortic Annular Plane and Coronary Ostia from Multidetector Computed Tomography. (28th May 2020)

• Record Type: Journal Article
• Authors: Astudillo, Patricio; Mortier, Peter; Bosmans, Johan; De Backer, Ole; de Jaegere, Peter; Iannaccone, Francesco; De Beule, Matthieu; Dambre, Joni
• Other Names: Musiałek Piotr Academic Editor.
• Is Part Of: Journal of interventional cardiology. Volume 2020(2020)
• Publication Date: 2020-05-28
• Subjects: Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.1206
• DOI: 10.1155/2020/9843275
• Languages: English
• ISSNs: 0896-4327

Abstract

Abstract : Anatomic landmark detection is crucial during preoperative planning of transcatheter aortic valve implantation (TAVI) to select the proper device size and assess the risk of complications. The detection is currently a time-consuming manual process influenced by the image quality and subject to operator variability. In this work, we propose a novel automatic method to detect the relevant aortic landmarks from MDCT images using deep learning techniques. We trained three convolutional neural networks (CNNs) with 344 multidetector computed tomography (MDCT) acquisitions to detect five anatomical landmarks relevant for TAVI planning: the three basal attachment points of the aortic valve leaflets and the left and right coronary ostia. The detection strategy used these three CNN models to analyse a single MDCT image and yield three segmentation volumes as output. These segmentation volumes were averaged into one final segmentation volume, and the final predicted landmarks were obtained during a postprocessing step. Finally, we constructed the aortic annular plane, defined by the three predicted hinge points, and measured the distances from this plane to the predicted coronary ostia (i.e., coronary height). The methodology was validated on 100 patients. The automatic landmark detection was able to detect all the landmarks and showed high accuracy as the median distance between the ground truth and predictions is lower than the interobserver variations (1.5 mm [1.1–2.1], 2.0 mm [1.3–2.8] with a paired difference −0.5 ± 1.3 mm and p value <0.001). Furthermore, a high correlation is observed between predicted and manually measured coronary heights (for both R 2 = 0.8). The image analysis time per patient was below one second. The proposed method is accurate, fast, and reproducible. Embedding this tool based on deep learning in the preoperative planning routine may have an impact in the TAVI environments by reducing the time and cost and improving accuracy.