Paper
20 March 2015 Automatic generation of endocardial surface meshes with 1-to-1 correspondence from cine-MR images
Yi Su, S.-K. Teo, C. W. Lim, L. Zhong, R. S. Tan
Author Affiliations +
Abstract
In this work, we develop an automatic method to generate a set of 4D 1-to-1 corresponding surface meshes of the left ventricle (LV) endocardial surface which are motion registered over the whole cardiac cycle. These 4D meshes have 1- to-1 point correspondence over the entire set, and is suitable for advanced computational processing, such as shape analysis, motion analysis and finite element modelling. The inputs to the method are the set of 3D LV endocardial surface meshes of the different frames/phases of the cardiac cycle. Each of these meshes is reconstructed independently from border-delineated MR images and they have no correspondence in terms of number of vertices/points and mesh connectivity. To generate point correspondence, the first frame of the LV mesh model is used as a template to be matched to the shape of the meshes in the subsequent phases. There are two stages in the mesh correspondence process: (1) a coarse matching phase, and (2) a fine matching phase. In the coarse matching phase, an initial rough matching between the template and the target is achieved using a radial basis function (RBF) morphing process. The feature points on the template and target meshes are automatically identified using a 16-segment nomenclature of the LV. In the fine matching phase, a progressive mesh projection process is used to conform the rough estimate to fit the exact shape of the target. In addition, an optimization-based smoothing process is used to achieve superior mesh quality and continuous point motion.
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Yi Su, S.-K. Teo, C. W. Lim, L. Zhong, and R. S. Tan "Automatic generation of endocardial surface meshes with 1-to-1 correspondence from cine-MR images", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941432 (20 March 2015); https://doi.org/10.1117/12.2081832
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Magnetic resonance imaging

Feature extraction

Motion models

Shape analysis

3D modeling

Image segmentation

Motion analysis

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