Article ID Journal Published Year Pages File Type
4947215 Neurocomputing 2017 19 Pages PDF
Abstract
Cardiac fibers have a quite complex and interesting spatial structure and the myocardial fiber orientation provides a great effort on both cardiac research studies and further clinical applications. Diffusion magnetic resolution imaging is a unique imaging methodology for non-invasively investigating the myofiber architecture, e.g., diffusion tensor imaging. Accurate quantitative mapping of the cardiac fiber is still challenging. In this study, a statistical diffusion atlas for myofiber is constructed from seven subjects that were acquired by diffusion weighted imaging scans. The atlas reconstruction work is based on a multimodal statistical framework which involves most advances in image analysis methods: level-sets for image segmentation, multi-modal registration and symmetric diffeomorphic nonlinear registration, weighted diffusion tensor averaging and accurate quantitative analysis. Experimental results demonstrate that the myofiber helix angle variation is markedly correlated with the transmural myocardial wall depth and its values vary from 45° in the endocardium to −40° in the epicardium, whilst it is 26° in the endocardium and −29° in the epicardium from traditional averaged atlas. The proposed atlas construction approach can effectively retain the water diffusion anisotropic information, especially on the myocardial tissue edges, which is a common known limit of current techniques. Moreover, we find that fractional anisotropy (FA) for the proposed atlas (0.55 ± 0.10) was higher than that of averaged one (0.42 ± 0.05). Substantial differences were found between FA values (P = 0.02). Mean diffusivity (MD) from proposed atlas (0.76 ± 0.05 × 10−3 mm2/s) was significantly lower than that of average atlas (0.80 ± 0.12 × 10−3 mm2/s), P = 0.03. This study illustrates a quantitative description for the myofiber structure and may provide a powerful tool to assess the micro-structure of myocardial tissue.
Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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