Authors:

Ahhyun Lee

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About Paper:

Thoracic aortic aneurysm (TAA) is a degenerative aortopathy that is associated with significant morbidity and mortality. A standard technique for diagnosing and tracking TAA is the manual measurement of aortic diameters using transthoracic 2-D echocardiography, typically in the parasternal long axis (PSLAX) view. However, these measurements are susceptible to user variability due to the translation of the vessel and the pulsatile expansion and contraction of the aortic root during the cardiac cycle. The objective of my work is to help develop a novel automated aortic root echocardiography feature-tracking algorithm to overcome these constraints. This algorithm employs standard clinical 2-D echocardiography data to derive detailed properties of the aortic root, including diameter, strain, and strain rate at discrete locations. By stabilizing the translational movement of the aortic root within a standard PSLAX view, the algorithm tracks the aortic wall frame-by-frame and records in-plane aortic diameter tracings across multiple cardiac cycles. To enhance accessibility, an improved Graphical User Interface (GUI) based on this algorithm was developed using MATLAB. My efforts will enable efficient analysis of clinical data as well as easy integration into clinical workflows. Furthermore, it will provide a user-friendly interface that makes it simple for medical professionals to input data and quantify metrics, improving the efficiency of aortic aneurysm diagnosis and monitoring. Future efforts will involve improving the algorithm and implementing additional automation using machine learning techniques in order to increase diagnostic accuracy and efficiency. Keywords: Thoracic Aortic Aneurysm; Echocardiography; Feature-Tracking Algorithm; Graphical User Interface

Source:

Purdue University / 2024

Topics:

No topics listed

Co-authors:

Ahhyun Lee