An Approach to Multibeam Covariance Matrices for Adaptive Beamforming in Ultrasonography

被引:0
|
作者
Jensen, Are Charles [1 ]
Austeng, Andreas [1 ]
机构
[1] Univ Oslo, Dept Informat, N-0316 Oslo, Norway
关键词
461.1 Biomedical Engineering - 711.2 Electromagnetic Waves in Relation to Various Structures - 713 Electronic Circuits - 746 Imaging Techniques - 921 Mathematics;
D O I
10.1109/TUFFC.2012.2304
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Medical ultrasound imaging systems are often based on transmitting, and recording the backscatter from, a series of focused broadband beams with overlapping coverage areas. When applying adaptive beamforming, a separate array covariance matrix for each image sample is usually formed. The data used to estimate any one of these covariance matrices is often limited to the recorded backscatter from a single transmitted beam, or that of some adjacent beams through additional focusing at reception. We propose to form, for each radial distance, a single covariance matrix covering all of the beams. The covariance matrix is estimated by combining the array samples after a sequenced time delay and phase shift. The time delay is identical to that performed in conventional delay-and-sum beamforming. The performance of the proposed approach in conjunction with the Capon beamformer is studied on both simulated data of scenes consisting of point targets and recorded ultrasound phantom data from a specially adapted commercial scanner. The results show that the proposed approach is more capable of resolving point targets and gives better defined cyst-like structures in speckle images compared with the conventional delay-and-sum approach. Furthermore, it shows both an increased robustness to noise and an increased ability to resolve point-like targets compared with the more traditional per-beam Capon beamformer.
引用
收藏
页码:1139 / 1148
页数:10
相关论文
共 50 条
  • [31] URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust Adaptive Beamforming
    Luo, Tao
    Chen, Peng
    Cao, Zhenxin
    Zheng, Le
    Wang, Zongxin
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2023, 59 (05) : 5634 - 5645
  • [32] Comments on "theory and application of covariance matrix tapers for robust adaptive beamforming"
    Zatman, M
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2000, 48 (06) : 1796 - 1800
  • [33] Simple adaptive beamforming with only real weights based on covariance differencing
    Choi, Y.-H.
    ELECTRONICS LETTERS, 2007, 43 (10) : 552 - 554
  • [34] ROBUST ADAPTIVE BEAMFORMING BASED ON MULTI-DIMENSIONAL COVARIANCE FITTING
    Ruebsamen, Michael
    Gershman, Alex B.
    2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2010, : 2538 - 2541
  • [35] Robust adaptive beamforming via subspace for interference covariance matrix reconstruction
    Zhu, Xingyu
    Xu, Xu
    Ye, Zhongfu
    SIGNAL PROCESSING, 2020, 167
  • [36] Robust adaptive beamforming via subspace for interference covariance matrix reconstruction
    Zhu, Xingyu
    Xu, Xu
    Ye, Zhongfu
    Signal Processing, 2020, 167
  • [37] Adaptive beamforming based on covariance matrix reconstruction by exploiting interferences' cyclostationarity
    Li, Jie
    Wei, Gang
    Ding, Yuehua
    SIGNAL PROCESSING, 2013, 93 (09) : 2543 - 2547
  • [38] Covariance matrix reconstruction with iterative mismatch approximation for robust adaptive beamforming
    Duan, Yanliang
    Zhang, Shunlan
    Cao, Weiping
    JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS, 2021, 35 (18) : 2468 - 2479
  • [39] Robust adaptive beamforming based on interference covariance matrix sparse reconstruction
    Gu, Yujie
    Goodman, Nathan A.
    Hong, Shaohua
    Li, Yu
    SIGNAL PROCESSING, 2014, 96 : 375 - 381
  • [40] Robust adaptive beamforming based on covariance matrix reconstruction with RCB principle
    Li, Haoran
    Geng, Jun
    Xie, Junhao
    DIGITAL SIGNAL PROCESSING, 2022, 127