A fast minimum variance beamforming method using principal component analysis

Kyuhong Kim, Suhyun Park, Jungho Kim, Sung Bae Park, Mooho Bae

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

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.

Original languageEnglish
Article number6819209
Pages (from-to)930-945
Number of pages16
JournalIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Volume61
Issue number6
DOIs
StatePublished - Jun 2014

Fingerprint

Dive into the research topics of 'A fast minimum variance beamforming method using principal component analysis'. Together they form a unique fingerprint.

Cite this