摘要
超快超声平面波成像技术实现了超声的高帧频大视野同步采集,捕捉到更多有效原始信息,而传统滤波器在处理这种大视野数据方面有诸多不足。该文基于Casorati奇异值分解(Casorati-SVD)技术提出一种改进的自适应杂波抑制算法:首先,选取一个区域的原始平面波数据构建Casorati数据矩阵并进行奇异值分解;其次,根据分解后分量的多普勒频率和能量自适应匹配合适的滤波截止参数,抑制组织杂波和噪声并提取血流信号;最后,对每个区域重复前面的步骤并统计所有输出获取最终图像。该文分别在仿体、人体手臂动脉和家兔脑血流的回波信号上验证该算法抑制杂波的能力,这些实验结果表明,相比全局Casorati奇异值分解滤波器,这种改进的分区域自适应滤波算法将信噪比(SNR)提高4.4%~50%,对比信噪比(CNR)提高4.7%~55.9%。该技术实现了多普勒血流成像的空间自适应滤波,对临床血流成像的发展有重要意义。
By using compounded plane wave,it enables the high-frame-rate acquisition of synchronous ultrasonic samples in the all field of view.However,classical clutter filters fail to deal with these big synchronous imaging datasets.In this study,an improved adaptive clutter rejection algorithm based on Casorati Singular Value Decomposition(Casorati-SVD)is proposed to take full advantage of synchronous datasets.The first step is to construct a Casorati matrix based on a block of plane-wave data and perform singular value decomposition on this Casorati matrix.Then the key point is to adaptively determine the cufoff thresholds according to Doppler frequency and energy of component signals and the blood flow signal is extracted through auto-generated filter.Finally,adaptive SVD filtering on each block is performed and the final flow signals are reconstructed from all blocks.To assess its ability in noise suppression,the proposed method is applied to blood flow echos obtained from phantom,arm artery and rabbit brain.These results demonstrate the improved method has 4.4%to 50%higher Signal-to-Noise-Ratio(SNR)and 4.7%to 55.9%Contrast-to-Noise-Ratio(CNR)than conventional Casorati-SVD methods.In conclusion,this method realizes spatial adaptive filtering and can be significant for development of clinical blood flow imaging.
作者
徐依雯
杨晨
徐杰
焦阳
崔崤峣
XU Yiwen;YANG Chen;XU Jie;JIAO Yang;CUI Yaoyao(Suzhou GuoKe Ultra Medical Technology Co.Ltd,Suzhou 215163,China;Suzhou Institute of Biomedical Engineering and Technology,Chinese Academy of Sciences,Suzhou 215163,China;Department of Electronic Engineering and Information Science,University of Science and Technology of China,Hefei 230031,China;Academy for Engineering and Technology,Hehai University,Shanghai 200433,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2021年第8期2334-2342,共9页
Journal of Electronics & Information Technology
基金
国家自然科学基金(51805529)
江苏省重点研发计划(BE2017601,BE2017661)。
