Sparse Convolutional Beamforming for Ultrasound Imaging

TL;DR

This paper introduces a novel nonlinear beamformer called COBA that significantly improves ultrasound image resolution and contrast while reducing the number of array elements needed, validated through simulations and in vivo data.

Contribution

The paper presents a new nonlinear convolutional beamformer and sparse array configurations that maintain image quality with fewer elements, enhancing efficiency and performance in ultrasound imaging.

Findings

COBA outperforms DAS in resolution and contrast.

Sparse beamformers require only about the square root of elements used in DAS.

Proposed methods achieve comparable or better image quality with fewer elements.

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.