Estimation of aortic valve leaflets from 3D CT images using local shape dictionaries and linear coding

Liang Liang; Caitlin Martin; Qian Wang; Wei Sun; James Duncan

doi:10.1117/12.2206032

21 March 2016 Estimation of aortic valve leaflets from 3D CT images using local shape dictionaries and linear coding

Liang Liang, Caitlin Martin, Qian Wang, Wei Sun, James Duncan

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978432 (2016) https://doi.org/10.1117/12.2206032
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Aortic valve (AV) disease is a significant cause of morbidity and mortality. The preferred treatment modality for severe AV disease is surgical resection and replacement of the native valve with either a mechanical or tissue prosthetic. In order to develop effective and long-lasting treatment methods, computational analyses, e.g., structural finite element (FE) and computational fluid dynamic simulations, are very effective for studying valve biomechanics. These computational analyses are based on mesh models of the aortic valve, which are usually constructed from 3D CT images though many hours of manual annotation, and therefore an automatic valve shape reconstruction method is desired. In this paper, we present a method for estimating the aortic valve shape from 3D cardiac CT images, which is represented by triangle meshes. We propose a pipeline for aortic valve shape estimation which includes novel algorithms for building local shape dictionaries and for building landmark detectors and curve detectors using local shape dictionaries. The method is evaluated on real patient image dataset using a leave-one-out approach and achieves an average accuracy of 0.69 mm. The work will facilitate automatic patient-specific computational modeling of the aortic valve.

Citation Download Citation

Liang Liang, Caitlin Martin, Qian Wang, Wei Sun, and James Duncan "Estimation of aortic valve leaflets from 3D CT images using local shape dictionaries and linear coding", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978432 (21 March 2016); https://doi.org/10.1117/12.2206032

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