Complementary Ensemble Empirical Mode Decomposition Based Microwave Induced Thermoacoustic Image Reconstruction Method

Xin Shang, Shuangli Liu, Weijia Wan, Lei Liu
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Abstract

In this paper, we adopt a signal processing method based on complementary ensemble empirical mode decomposition (CEEMD) and singular value decomposition (SVD) to reconstruct the thermoacoustic image. Thermoacoustic signals are easily interfered by factors such as temperature and mixed with incoherent noise during propagation, both CEEMD and SVD have a good effect in extracting the main components of the signal and removing noise. The main idea of this method is denoising artifacts by decomposing the ultrasound signal received by the sensor into a series of intrinsic mode functions (IMFs), choosing the effective IMFs based on SVD. We tested a single tumor in the homogeneous media by numerical simulation. The peak signal-to-noise ratios of the thermoacoustic images reconstructed by the proposed method, and the other three methods are compared. The results indicate that the method of CEEMD combined with SVD has better performance. Validation based on experimental data will be carried out in the follow-up work.
基于互补系综经验模态分解的微波热声图像重建方法
本文采用基于互补系综经验模态分解(CEEMD)和奇异值分解(SVD)的信号处理方法对热声图像进行重构。热声信号在传播过程中容易受到温度等因素的干扰,并且混杂着非相干噪声,CEEMD和奇异值分解在提取信号主要成分和去除噪声方面都有很好的效果。该方法的主要思想是将传感器接收到的超声信号分解为一系列内禀模态函数(imf),并基于奇异值分解(SVD)选择有效的内禀模态函数来去除伪影。我们用数值模拟的方法测试了均匀介质中的单个肿瘤。对比了采用该方法重建的热声图像的峰值信噪比。结果表明,CEEMD与奇异值分解相结合的方法具有更好的性能。基于实验数据的验证将在后续工作中进行。
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