A fast minimum variance beamforming method using principal component analysis
- 21 May 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
- Vol. 61 (6), 930-945
- https://doi.org/10.1109/tuffc.2014.2989
Abstract
Minimum variance (MV) beamforming has been studied for improving the performance of a diagnostic ultrasound imaging system. However, it is not easy for the MV beamforming to be implemented in a real-time ultrasound imaging system because of the enormous amount of computation time associated with the covariance matrix inversion. In this paper, to address this problem, we propose a new fast MV beamforming method that almost optimally approximates the MV beamforming while reducing the computational complexity greatly through dimensionality reduction using principal component analysis (PCA). The principal components are estimated offline from pre-calculated conventional MV weights. Thus, the proposed method does not directly calculate the MV weights but approximates them by a linear combination of a few selected dominant principal components. The combinational weights are calculated in almost the same way as in MV beamforming, but in the transformed domain of beamformer input signal by the PCA, where the dimension of the transformed covariance matrix is identical to the number of some selected principal component vectors. Both computer simulation and experiment were carried out to verify the effectiveness of the proposed method with echo signals from simulation as well as phantom and in vivo experiments. It is confirmed that our method can reduce the dimension of the covariance matrix down to as low as 2 × 2 while maintaining the good image quality of MV beamforming.Keywords
This publication has 17 references indexed in Scilit:
- An approach to multibeam covariance matrices for adaptive beamforming in ultrasonographyIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2012
- A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrixIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2012
- A low-complexity data-dependent beamformerIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2011
- Eigenspace-based minimum variance beamforming applied to medical ultrasound imagingIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2010
- Beamspace adaptive beamforming for ultrasound imagingIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2009
- Benefits of minimum-variance beamforming in medical ultrasound imagingIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2009
- Finite sample approximation results for principal component analysis: A matrix perturbation approachThe Annals of Statistics, 2008
- A complex gradient operator and its application in adaptive array theoryIEE Proceedings H Microwaves, Optics and Antennas, 1983
- Signal cancellation phenomena in adaptive antennas: Causes and curesIEEE Transactions on Antennas and Propagation, 1982
- High-resolution frequency-wavenumber spectrum analysisProceedings of the IEEE, 1969