Artificial Intelligence-based Patient-specific Reconstruction of Aortic Root in Transcatheter Aortic Valve Replacement Patients

Affecting nearly 3% of adults above 65 years of age, aortic stenosis (AS) is a disease that involves narrowing of the aortic valve opening, which restricts the blood flow from the heart. If severe, the stenotic valve is typically replaced with a bioprosthetic valve via either an open-heart surgery or a transcatheter aortic valve replacement (TAVR), a less invasive procedure recommended for patients at increased risk of mortality with conventional surgery. A TAVR requires a careful procedural planning to consider the patients' aortic root anatomy and avoid any adverse effects that may be life-threatening. In order to accurately visualize the anatomical structures of the aortic root and understand the fluid dynamics of the blood flow through the valve, a 3D reconstruction of the root geometry can be formulated by segmenting the anatomy from patients’ computer tomography (CT) images. Yet, a manual segmentation of the aortic root is a process both demanding and time-consuming. Instead, an efficient and accurate modeling of the aortic root geometry can be achieved using a set of unique aortic landmarks that are localized on CT images. Accordingly, this research aims to utilize image processing and artificial intelligence-based methods such as convolutional neural network (CNN) to 1) automatically detect the aortic landmarks of interest on CT images and 2) construct a 3D patient-specific reconstruction of the aortic root using the detected landmarks. This study presents a method that can be utilized in both assisting the pre-procedural planning of TAVR through fast, automatic segmentation of the aortic root structures and bolstering the study of fluid and structural dynamics in TAVR patients through a robust, CT image-guided patient-specific reconstruction of the aortic root anatomy.

Date

2022-04-28

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Text

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