Fast Minimum Variance Beamforming Using Principal Component Analysis
Journal
IEEE Transaction on Ultrasonics, Ferroelectrics, and Frequency Control Society(UFFC)
Date
2014.06.01
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 the dimensionality reduction using the 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 almost in the same way as in the 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 Field II simulation and test phantom experiment. It is confirmed that our method can reduce the dimension of the covariance matrix down to 2 x 2 while maintaining the good image quality of the MV beamforming