基于空间平滑的超声自适应波束形成算法实现
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摘要
针对超声回波信号的强相关性限制传统自适应波束形成算法在超声成像领域的应用以及传统超声成像质量不高的问题,文中采用了基于前后向空间平滑的超声自适应波束形成算法(FBMV)。利用前后向空间平滑技术有效去除回波信号相关性,使得自适应波束形成算法能应用于超声成像中。文中利用最小方差(MV)自适应波束形成算法提高了超声成像的质量。通过对前后向平滑技术原理的仔细分析及改进,给出了FPGA实现整个算法的详细步骤。最后对阵列方向图和信号功率谱两方面进行了仿真,仿真结果表明,该算法能以较小分辨率牺牲为代价换取超声成像对比度的显著提升。
Since the application of the adaptive beamforming algorithm in the field of ultrasonic imaging was limited due to the strong correlation of ultrasonic echo signals and the quality of traditional ultrasonic imaging was not high,an ultrasonic adaptive beamforming algorithm based on FBMV was used in this study. The correlation of echo signals was eliminated by using the forward-backward spatial smoothing technology,which made adaptive beamforming algorithm applied in ultrasonic imaging. The minimum variance adaptive beamforming algorithm was used to improve the quality of ultrasonic imaging. Through the careful analysis and improvement of the principle of the forward-backward smoothing technology,the detailed steps of the algorithm based on FPGA were given. Simulation of the array pattern and the signal power spectrum showed that the algorithm could make a significant improvement in the contrast of ultrasound imaging at the expense of smaller resolution sacrifices.
Since the application of the adaptive beamforming algorithm in the field of ultrasonic imaging was limited due to the strong correlation of ultrasonic echo signals and the quality of traditional ultrasonic imaging was not high,an ultrasonic adaptive beamforming algorithm based on FBMV was used in this study. The correlation of echo signals was eliminated by using the forward-backward spatial smoothing technology,which made adaptive beamforming algorithm applied in ultrasonic imaging. The minimum variance adaptive beamforming algorithm was used to improve the quality of ultrasonic imaging. Through the careful analysis and improvement of the principle of the forward-backward smoothing technology,the detailed steps of the algorithm based on FPGA were given. Simulation of the array pattern and the signal power spectrum showed that the algorithm could make a significant improvement in the contrast of ultrasound imaging at the expense of smaller resolution sacrifices.
引文
[1]郑驰超,彭虎,韩志会.基于空间广义相干系数加权的医学合成孔径超声成像[J].声学学报,2011,36(1):637-641.
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[6]谢建平,王瑞,何熊熊,等.利用导向矢量旋转和联合迭代优化的自适应波束形成算法研究[J].声学学报,2016,41(3):343-352.
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[10]Asl B M,Mahloojifar A. Eigenspace-based minimum variance beamforming applied to medical ultrasound imaging[J].IEEE Transactions on Ultrasonics,Ferroelectrics and Frequency Control,2010,57(11):2381-2390.
[11] Mehdizadeh S,Austeng A,Johansen T F,et al. Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues[J]. IEEE Transactions on Medical Imaging,2012,31(10):1912-1928.
[12]Landau L,De Lamare R C,Haardt M. Robust adaptive beamforming alogrithms using the constrained constant modulus criterion[J]. IET Signal Processing,2014,8(5):447-457.
[13]Nuan S,Alokozai W U,Lamare R C,et al. Adaptive widely linear Reduced-Rank beamforming based on joint iterative optimization[J]. IEEE Signal Processing Letter,2014,21(3):265-269.
[14]王永良,陈辉,彭万宁,等.空间谱估计理论与算法[M].北京:清华大学出版社,2005.
[15]段维,舒勤. MUSIC算法与空间平滑类算法对相干信源DOA估计比较[J].计算机应用,2013,33(S2):318-320.
[16] Li Q,Zhou T,Chen X. Adaptive ultrasound imaging using forward-Backward minimum variance beamforming and coherence weighting[C]. Wuhan:International Conference on Intelligent Computation and Bio-Medical Instrumention,2011.
[2]吴文寿,蒲杰,吕效.最小方差波束形成与广义相干系数融合的医学超声成像方法[J].声学学报,2011,36(1):66-72.
[3]许琴.超声成像中波束形成算法研究[D].重庆:重庆大学,2012.
[4]王平,许琴,范文政,等.超声成像中基于特征空间的前后向自小方差波束形成[J].声学学报,2013,38(1):65-70.
[5]刘婷婷,周浩,郑音飞.特征空间和符号相干系数融合的最小方差超声波束形成[J].声学学报,2015,40(6):855-862.
[6]谢建平,王瑞,何熊熊,等.利用导向矢量旋转和联合迭代优化的自适应波束形成算法研究[J].声学学报,2016,41(3):343-352.
[7] Synnevag J F,Austeng A. Adaptive beamforming applied to medical ultrasound imaging[J]. IEEE Transactions on Ultrasonics,Ferroelectrics and Frequency Control,2007,54(8):1606-1613.
[8] Asl B M,Mahloojifar A. Minimum variance beamforming combined with adaptive coherence weighting applied to medical ultrasound imaging[J]. IEEE Transactions on Ultrasonics,Ferroelectrics and Frequency Control,2009,56(9):1923-1931.
[9] Jian Li,Stoica P,Zhisong Wang. On robust capon beamforming and diagonal loading[J]. IEEE Transactions on Ultrasonics,Ferroelectrics and Frequency Control,2003,51(7):1702-1714.
[10]Asl B M,Mahloojifar A. Eigenspace-based minimum variance beamforming applied to medical ultrasound imaging[J].IEEE Transactions on Ultrasonics,Ferroelectrics and Frequency Control,2010,57(11):2381-2390.
[11] Mehdizadeh S,Austeng A,Johansen T F,et al. Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues[J]. IEEE Transactions on Medical Imaging,2012,31(10):1912-1928.
[12]Landau L,De Lamare R C,Haardt M. Robust adaptive beamforming alogrithms using the constrained constant modulus criterion[J]. IET Signal Processing,2014,8(5):447-457.
[13]Nuan S,Alokozai W U,Lamare R C,et al. Adaptive widely linear Reduced-Rank beamforming based on joint iterative optimization[J]. IEEE Signal Processing Letter,2014,21(3):265-269.
[14]王永良,陈辉,彭万宁,等.空间谱估计理论与算法[M].北京:清华大学出版社,2005.
[15]段维,舒勤. MUSIC算法与空间平滑类算法对相干信源DOA估计比较[J].计算机应用,2013,33(S2):318-320.
[16] Li Q,Zhou T,Chen X. Adaptive ultrasound imaging using forward-Backward minimum variance beamforming and coherence weighting[C]. Wuhan:International Conference on Intelligent Computation and Bio-Medical Instrumention,2011.