北京生物医学工程

基于小波稀疏的磁性纳米粒子成像算法研究

Research on magnetic particle imaging algorithm based on wavelet sparse

作者：张玉录柯丽杜强赵宇楠祖婉妮

单位：沈阳工业大学电气工程学院(沈阳 110870) <p>通信作者：柯丽。E-mail:keli@sut.edu.cn</p>

分类号：R318.04

出版年·卷·期（页码）：2021·40·4（337-345）

摘要：

目的为了实现磁性纳米粒子成像(magnetic particle imaging, MPI)中粒子浓度空间分布的快速精准成像，针对系统矩阵成像方法所构建矩阵方程的求解问题，本文提出一种基于小波稀疏的MPI算法。方法首先通过仿真从基于零场线的开放式MPI电磁系统中获得MPI信号构建矩阵方程；然后在经典代数重建算法(algebraic reconstruction technique, ART)每次迭代后均采用小波变换提取图像中粒子分布边缘的非平稳特征，结合阈值算子稀疏运算去除图像中的干扰信号，实现粒子浓度空间分布成像；最后用峰值信噪比参数(peak signal-to-noise ratio, PSNR)对不同噪声下的成像结果进行分析。结果当系统信噪比为30 dB、20 dB、10 dB时，基于小波稀疏的MPI算法在快速收敛的前提下，所成图像的PSNR参数相较经典代数重建算法分别提升了67.83%、18.66%、8.05%。结论在低噪声水平下，基于小波稀疏的MPI算法可在短时间内实现粒子分布状况的高质量成像。

Objective In order to achieve rapid and accurate imaging of the spatial distribution of particle concentration in magnetic particle imaging, aiming at the problem of solving the matrix equation constructed by the system matrix imaging method, this paper proposes a magnetic particle imaging algorithm based on wavelet sparseness. Methods First, obtain the MPI signal from the open MPI electromagnetic system based on the free field line to construct the matrix equation through simulation;Then after each iteration of the classical algebraic reconstruction technique, wavelet transform is used to extract the non-stationary features of the edge of the particle distribution in the image,combine the threshold operator sparse operation to remove the interference signal in the image and realize the imaging of the spatial distribution of particle concentration;Finally, the peak signal-to-noise ratio parameter is used to analyze the imaging results under different noises. Results When the system signal-to-noise ratio is 30dB, 20dB, 10dB, the MPI algorithm based on wavelet sparseness under the premise of rapid convergence, the PSNR parameters of the resulting image are improved by 67.83%, 18.66%, and 8.05% respectively compared with the classic algebraic reconstruction technique. Conclusions Under low noise level, the MPI algorithm based on wavelet sparseness can achieve high-quality imaging of particle distribution in a short time.

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