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 |Pages: 11
DOI: 10.4018/IJeC.304033
OnDemand:
(Individual Articles)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

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.
Article Preview
Top

1 Introduction

The most important part of human body is the heart, and its proper function is must for the proper function of human body. Along with being most important it’s equally complicated and works continually from the birth till death of human body. A malfunction of this organ may lead to human death. In recent years, the number of deaths caused by cardiovascular diseases accounted for 38.7 percent in rural areas and 41.1 percent in urban areas. In 2017 the total number of deaths because of this reason is almost 17.80 Million and it the biggest cause of death as shown in figure 1 (Hannah Ritchie 2018).

In order to reduce these numbers exhaustive efforts must be made and being made by the researchers across the globe. The continuous efforts made by various researchers have raised an importance of the comprehensive understanding of cardiac structure and function in health and disease. This can be achieved with the experimental studies and investigation of myocardium in disease and health for both humans and animal from molecular to whole body level. These efforts may become as a preventive measure form the cardiovascular disease. Myocardial fibrosis is an important pathological feature of various cardiovascular diseases. Myocardial fibrosis reflects the disorder of the volume and arrangement of cardiac muscle fibers, which affects the normal heart function and may lead to myocardial infarction, heart failure, myocarditis, myocardial ischemia, myocardial hypertrophy and other cardiovascular diseases. If the myocardial fibers can be accurately detected and reconstructed, the structures of myocardial fibers can be used to diagnose cardiac diseases and analyze their causes. Therefore, myocardial fiber reconstruction has important theoretical research value and clinical application value. Myocardium may be defined as a thick layer between the epicardium and endocardium. It is an organization of connective tissue, cardiac muscle and a very high density of capillaries. These muscle fibers are arranges in various sheets which wraps around the ventricle with continuous changing orientation. This enables the ventricle to contract in several directions simultaneously.

Magnetic resonance diffusion imaging can obtain the water differences in living tissues and the microscopic diffusion information of water molecules in a non-invasive way, making it possible to achieve myocardial fiber reconstruction under non-invasive conditions (Ping Chen et al 2018). Magnetic resonance diffusion (mri) has been extensively studied in the reconstruction of white matter fibers. As a non-invasive method for the detection of tissue microstructure, the application of this technique in the reconstruction of cardiac muscle fibers has also aroused the interest of researchers. However, efficiency and accuracy in the reconstruction of heart muscle fibers remain a challenging problem in this field. The basic idea of adaptive particle filtering proposed in this paper is to use particle filtering technology to estimate the trend of local fibers.

Figure 1.

Number of deaths by cause, World

IJeC.304033.f01

The rest of the literature is organized as follows. In next section a detail description of the literatures is provided which were referred by the authors for understanding the scope and advancement of this research. In section three the research methods used in this work is explained thoroughly. In subsections sections authors have discussed about the Myocardial fiber in detail, in the later part of this section the Analysis of particle filter adaptive mechanism is discussed, so that the readers can develop the required knowledge for understanding the proposed work. In next section the authors have displayed a Comparison experiment of myocardial fiber reconstruction under different particle Numbers, with the help of various tables and graphs for validating the novelty of the proposed methods. In last section the paper is concluded citing the contribution of authors and future scope of the work.

Complete Article List

Search this Journal:
Reset
Volume 20: 1 Issue (2024)
Volume 19: 7 Issues (2023)
Volume 18: 6 Issues (2022): 3 Released, 3 Forthcoming
Volume 17: 4 Issues (2021)
Volume 16: 4 Issues (2020)
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing