Sparse Convolutional Beamforming for Ultrasound Imaging

Abstract

The standard technique used by commercial medical ultrasound systems to form B-mode images is delay and sum (DAS) beamforming. However, DAS often results in limited image resolution and contrast, which are governed by the center frequency and the aperture size of the ultrasound transducer. A large number of elements leads to improved resolution but at the same time increases the data size and the system cost due to the receive electronics required for each element. Therefore, reducing the number of receiving channels while producing high quality images is of great importance. In this paper, we introduce a nonlinear beamformer called COnvolutional Beamforming Algorithm (COBA), which achieves significant improvement of lateral resolution and contrast. In addition, it can be implemented efficiently using the fast Fourier transform. Based on the COBA concept, we next present two sparse beamformers with closed form expressions for the sensor locations, which result in the same beam pattern as DAS and COBA while using far fewer array elements. Optimization of the number of elements shows that they require a minimal number of elements which is on the order of the square root of the number used by DAS. The performance of the proposed methods is tested and validated using simulated data, phantom scans and in vivo cardiac data. The results demonstrate that COBA outperforms DAS in terms of resolution and contrast and that the suggested beamformers offer a sizable element reduction while generating images with an equivalent or improved quality in comparison to DAS.Index Terms-Medical ultrasound, array processing, beamforming, contrast resolution, sparse arrays, beam pattern.

Deep Learning in Ultrasound Imaging

“…Second, channel data is extremely large, in particular for large arrays or matrix transducers and when sampled at the Nyquist rate. This may be alleviated significantly by leveraging sub-Nyquist sampling schemes [3], [14], [15], [17], [55], permitting high-end processing of lowrate channel data after (wireless) transfer to a remote (or cloud) processor. Such a new scheme, with a wireless probe that streams low-rate channel data for subsequent deep learning in the cloud, would open up many new possibilities for intelligent image formation and advanced processing in ultrasonography.…”

Subsampling Approaches for Compressed Sensing with Ultrasound Arrays in Non-Destructive Testing

“…The effort in sparse array design and optimal sensor position is largely dedicated to choosing a performance metric that promotes the desired imaging behavior. These metrics include, but are not limited to, beam patterns [14,15,17], number of virtual sensors [18,19], mutual coherence [8], information-theoretic quantities [16], and data-driven quantities [20]. Similarly to [22], the approach in this paper focuses on minimizing the variance of the estimate of the location of point-like scatterers.…”

“…To this end, in the field of acoustics recent works have focused on the greedy optimization of cost functions such as the mutual information between sensor locations that have already been assigned and those that are still open [16], or the average size of the main lobe in a Region of Interest (ROI) over which the probability of finding a source is non-uniform [17]. When paired with convolutional beamforming, the elements in a pair of co-prime arrays may also be placed such that the resulting co-array has a larger aperture and effectively contains all of its elements [18].…”

Sparse Convolutional Beamforming for Wireless Ultrasound

“…Therefore, we can equivalently apply lateral convolution on the delayed signals, and then sum the elements. To achieve a beampattern with desired properties we examine the beampattern of the DAS beamformer [25]…”