Deep Learning Applied to Beamforming in Synthetic Aperture Ultrasound

TL;DR

This paper introduces a deep neural network approach to ultrasound beamforming that improves image resolution and contrast, and can reduce array size, validated on simulated and real cardiac data.

Contribution

It applies deep learning specifically to the beamforming stage in ultrasound imaging, demonstrating improved image quality and array efficiency over standard methods.

Findings

Enhanced image resolution and contrast achieved.

Reduced array size without loss of image quality.

Effective generalization from simulated to real data.

Abstract

Deep learning methods can be found in many medical imaging applications. Recently, those methods were applied directly to the RF ultrasound multi-channel data to enhance the quality of the reconstructed images. In this paper, we apply a deep neural network to medical ultrasound imaging in the beamforming stage. Specifically, we train the network using simulated multi-channel data from two arrays with different sizes, using a variety of direction of arrival (DOA) angles, and test its generalization performance on real cardiac data. We demonstrate that our method can be used to improve image quality over standard methods, both in terms of resolution and contrast. Alternatively, it can be used to reduce the number of elements in the array, while maintaining the image quality. The utility of our method is demonstrated on both simulated and real data.