A Sibelobe Suppressing Beamformer for Coherent Plane Wave Compounding

Abstract

Contrast degradation is a critical problem in ultrasound plane wave imaging (PWI) resulting from signals leakage from the sidelobes. An ideal sidelobe reduction method may enhance the contrast without remarkably increasing computational load. To this end, we introduce a new singular value decomposition (SVD) sidelobe reduction beamformer for coherent plane wave compounding (CPWC) based on a previous work. The SVD takes advantage of the benefits of the different features of the mainlobe and sibelobe in terms of spatio-angular coherence and removes the sidelobes before the final coherent summation. This SVD-based method provides a three-dimensional approach (2D in the space and 1D in the angle) while the computation load is kept satisfactory by a dimension-reduced operation before the SVD. To directly observe the sidelobe level, we demonstrate the performance of our SVD method with a point spread function (PSF) simulation. Compared to CPWC, our method shows a 6.2 dB reduction in the peak sidelobe level (PSL). We also applied our method to the anechoic cyst inside the speckle for the imaging contrast. Both in the simulation and phantom studies, our method enhances the contrast-to-noise ratio (CNR) by more than 10%. Therefore, this new beamformer can be an efficient way to suppress sidelobes in PWI.

Sidelobe reduction for plane wave compounding with a limited frame number

“…As shown in [15], our last proposed method has a satisfactory side lobe suppressing performance when the steering angles are no less than 10. The new method aims to further reduce the number of frames required for compounding, thus we choose 7 angles for our experiments.…”

“…Because the dimension of the covariance matrix for subarray size L is L  ×  L , its inverse needs operations of order O( L 3 ) using Gaussian elimination [30]. The main computational amount of the SVD filter method in [15] occurs on the SVD of the covariance matrix, which requires O( N 3 ) floating operations by using the Golub–Reinsch algorithm [31]. Our method constructs the bi-directional connectivity matrix via the sparse representation using Eqs.…”

“…Apart from the normal PWC and our proposed effective distance-based method, a SVD filter method [15] is also included in our experiment for comparison. This method is also designed for the PWC, and it performs well with more than 10 steering angles.…”

“…This method is also designed for the PWC, and it performs well with more than 10 steering angles. In this method, the threshold for the eigen vector number is set to 1 as the [15] does.…”

“…On this occasion, the PWI loses its frame rate advantage over a focused linear-scan acquisition. To reduce the PWC transmissions, our group has developed a sidelobe reduction beamformer for the PWC based on the singular value decomposition (SVD) filter [15]. The key point is that the sidelobe artefacts from different angles have poor coherence [7].…”