Low-complexity adaptive minimum variance ultrasound beam-former based on diagonalization. (September 2020)

• Record Type: Journal Article
• Authors: Afrakhteh, Sajjad; Behnam, Hamid
• Is Part Of: Biomedical signal processing and control. Volume 62(2020)
• Publication Date: 2020-09
• Subjects: Minimum variance -- Delay and sum -- Low-complexity adaptive minimum variance -- Beamforming -- Resolution -- Contrast -- Complexity -- Ultrasound imaging; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102110
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A low complexity minimum variance is suggested for the beam-former. The noticeable feature of the proposed method is its simplicity. By using this technique, the Minimum Variance runtime is reduced significantly. This simple technique can be a big step towards adaptive real-time imaging. The complexity is reduced from order of triple L to order of (L × log (L)). Abstract: Background: Despite the high capability of the minimum variance (MV) beam-former in improving image quality compared to the delay and sum (DAS), its high complexity, which is due to L × L covariance matrix inversion and calculation, limits its usage for real-time applications. The complexity of MV is in the order of O(L 3 ). Method: A low-complexity adaptive MV (LCAMV) is suggested in to reduce the complexity of the MV beam-former via the covariance matrix diagonalization. So, the proposed method focuses on two aspects: One is to reduce the complexity due to covariance matrix calculation and the other to reduce the MV complexity caused by the inversion. Thus, the MV complexity is significantly reduced, although the resulted image of LCAMV is similar to that of MV. Results: The LCAMV technique is applied to the simulated phantoms and real data to verify its performance and to compare it with other popular beamforming techniques. Statistical parameters of the image quality for all experiments were calculated to check the image quality. Also, the run time analysis showed that the difference between the values of the MV and LCAMV statistical image quality parameters was less than 2%, while the speed of LCAMV was about 4 times faster. Conclusion: Results indicate that using LCAMV, not only the resolution and contrast are not changed significantly, but also the complexity is reduced from O ( L 3 ) to O ( L × l o g ( L ) ) . Thus, this simple technique can be a big step towards adaptive real-time imaging.