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A Method for the Reconstruction of Myocardial Fiber Structure in Diffusivity Adaptive Imaging Based on Particle Filter

A Method for the Reconstruction of Myocardial Fiber Structure in Diffusivity Adaptive Imaging Based on Particle Filter

Jun Yin, Xuan Gao, Min Wu, Yan Liang
Copyright: © 2022 |Volume: 18 |Issue: 2 |Pages: 11
ISSN: 1548-3673|EISSN: 1548-3681|EISBN13: 9781799893875|DOI: 10.4018/IJeC.304033
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MLA

Yin, Jun, et al. "A Method for the Reconstruction of Myocardial Fiber Structure in Diffusivity Adaptive Imaging Based on Particle Filter." IJEC vol.18, no.2 2022: pp.1-11. http://doi.org/10.4018/IJeC.304033

APA

Yin, J., Gao, X., Wu, M., & Liang, Y. (2022). A Method for the Reconstruction of Myocardial Fiber Structure in Diffusivity Adaptive Imaging Based on Particle Filter. International Journal of e-Collaboration (IJeC), 18(2), 1-11. http://doi.org/10.4018/IJeC.304033

Chicago

Yin, Jun, et al. "A Method for the Reconstruction of Myocardial Fiber Structure in Diffusivity Adaptive Imaging Based on Particle Filter," International Journal of e-Collaboration (IJeC) 18, no.2: 1-11. http://doi.org/10.4018/IJeC.304033

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Abstract

In order to explore the cause of characteristic change and pathological variation of myocardial fiber structure, the posterior probability distribution of fiber direction was described. To solve the problems of low computational efficiency and slow convergence of traditional particle filter, an adaptive particle filter myocardial fiber reconstruction algorithm based on diffusion anisotropy is proposed. This algorithm dynamically adjusts the number of particles and the disturbance intensity at the prediction stage according to the diffusion anisotropy values at different body elements. While ensuring the quality of state estimation, the computational complexity of the algorithm is reduced and the operating efficiency of the system is significantly improved. The experimental results show that the proposed method has strong anti-noise ability. While improving the accuracy of fiber reconstruction, the computational cost of the system decreases by 50%, which significantly improves the efficiency of the system. The proposed algorithm is good over traditional PF and STL approaches.

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