Semi-Automated Construction of Patient-Specific Aortic Valves from Computed Tomography Images.
Dan LiorCharles PuelzColin EdwardsSilvana MolossiBoyce E GriffithRavi K BirlaCraig G RusinPublished in: Annals of biomedical engineering (2022)
This paper presents a semi-automatic method for the construction of volumetric models of the aortic valve using computed tomography angiography images. Although the aortic valve typically cannot be segmented directly from a computed tomography angiography image, the method described herein uses manually selected samples of an aortic segmentation derived from this image to inform the construction. These samples capture certain physiologic landmarks and are used to construct a volumetric valve model. As a demonstration of the capabilities of this method, valve models for 25 pediatric patients are created. A selected valve anatomy is used to perform fluid-structure interaction simulations using the immersed finite element/difference method with physiologic driving and loading conditions. Simulation results demonstrate this method creates a functional valve that opens and closes normally and generates pressure and flow waveforms that are similar to those observed clinically.
Keyphrases
- high throughput
- aortic valve
- deep learning
- transcatheter aortic valve replacement
- aortic stenosis
- transcatheter aortic valve implantation
- aortic valve replacement
- single cell
- computed tomography
- convolutional neural network
- coronary artery
- heart failure
- pulmonary hypertension
- left ventricular
- image quality
- pulmonary artery
- monte carlo