Investigation on Imaging Features and Clinical Significance of Cardiac CT in Comprehensive Evaluation of Aortic Valve and Root before Percutaneous Aortic Valve Replacement

Abstract

Medical imaging feature analysis is the basis of medical image processing and analysis. The solution of this problem not only directly affects the successful application of computer graphics and image technology in medicine but also has important theoretical and practical significance. In this paper, the imaging characteristics and clinical significance are discussed by studying the comprehensive evaluation of aortic valve and root before aortic valve replacement. In recent years, preoperative comprehensive evaluation of the aortic valve and root has been gradually carried out. Compared with traditional methods, minimally invasive surgery brings more accurate diagnosis to patients, quick recovery and discharge after surgery, and less pain. This study retrospectively includes patients with severe aortic stenosis who underwent TAVR with routine computed tomography. Based on CT images, the determination and grouping of bicuspid aortic valve and tricuspid aortic valve were completed. Thirteen cross-sectional levels of the aorta-iliac-femoral vascular access were completed. The results showed that 3 people had stroke (17.6%) and 5 people had myocardial infarction (29.4%) during the follow-up period. Atrial fibrillation occurred in 5 patients (29.4%), permanent pacemaker implantation was performed in 1 patient (5.9%), and acute kidney injury occurred in 7 patients (41.2%). No patient died due to surgery-related causes, and the analysis of imaging features and clinical significance in the preoperative comprehensive evaluation of the aortic valve and root played a crucial role. In the training stage, the principal component analysis method was used to train the shape, and the model of the shape intensity of the aortic valve and the shape change of each principal component was obtained. The most probable aortic valve region in the target image was obtained by matching the similarity of all atlases, and the correct aortic valve segmentation was obtained by using the first level set of shape intensity. The experimental part verified the accuracy of the algorithm.