北京生物醫(yī)學(xué)工程

基于小波稀疏的磁性納米粒子成像算法研究

Research on magnetic particle imaging algorithm based on wavelet sparse

作者：張玉錄柯麗杜強(qiáng) 趙宇楠祖婉妮

單位：沈陽工業(yè)大學(xué)電氣工程學(xué)院(沈陽 110870) <p>通信作者：柯麗。E-mail:[email protected]</p>

關(guān)鍵詞：磁性納米粒子成像；代數(shù)重建算法；小波變換；閾值算子；峰值信噪比

分類號(hào)：R318.04

出版年·卷·期（頁碼）：2021·40·4（337-345）

摘要：

目的為了實(shí)現(xiàn)磁性納米粒子成像(magnetic particle imaging, MPI)中粒子濃度空間分布的快速精準(zhǔn)成像，針對(duì)系統(tǒng)矩陣成像方法所構(gòu)建矩陣方程的求解問題，本文提出一種基于小波稀疏的MPI算法。方法首先通過仿真從基于零場(chǎng)線的開放式MPI電磁系統(tǒng)中獲得MPI信號(hào)構(gòu)建矩陣方程；然后在經(jīng)典代數(shù)重建算法(algebraic reconstruction technique, ART)每次迭代后均采用小波變換提取圖像中粒子分布邊緣的非平穩(wěn)特征，結(jié)合閾值算子稀疏運(yùn)算去除圖像中的干擾信號(hào)，實(shí)現(xiàn)粒子濃度空間分布成像；最后用峰值信噪比參數(shù)(peak signal-to-noise ratio, PSNR)對(duì)不同噪聲下的成像結(jié)果進(jìn)行分析。結(jié)果當(dāng)系統(tǒng)信噪比為30 dB、20 dB、10 dB時(shí)，基于小波稀疏的MPI算法在快速收斂的前提下，所成圖像的PSNR參數(shù)相較經(jīng)典代數(shù)重建算法分別提升了67.83%、18.66%、8.05%。結(jié)論在低噪聲水平下，基于小波稀疏的MPI算法可在短時(shí)間內(nèi)實(shí)現(xiàn)粒子分布狀況的高質(zhì)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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