A Low-Complexity Minimum Variance Algorithm Combined with Eigenvalue Decomposition for Ultrasound Imaging
Author:
Affiliation:
1. State Key Lab. of Power Transmission Equip. & System Security and New Tech., Chongqing University, Chongqing, China
2. State Grid Fujian Electric Power Co., Ltd. Maintenance Branch, Fujian Province, China
Funder
NSFC
Publisher
ACM
Reference9 articles.
1. Real-Time Monitoring and Quantitative Evaluation of Cavitation Bubbles Induced by High Intensity Focused Ultrasound Using B-Mode Imaging
2. Sequential beamforming for synthetic aperture imaging
3. Shen C. C. Xing Y. Q. and Gency J. 2016. Autocorrelation-based generalized coherence factor for low-complexity adaptive beamforming. Ultrasonics. 72(Dec. 2016) 117--183. Shen C. C. Xing Y. Q. and Gency J. 2016. Autocorrelation-based generalized coherence factor for low-complexity adaptive beamforming. Ultrasonics. 72(Dec. 2016) 117--183.
4. 2012. A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix;Babak M. A.;IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.
5. 2016. Computationally efficient adaptive beamformer for ultrasound imaging based on QR decomposition;Jongin P.;IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.
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