IMPROVING LATERAL RESOLUTION IN 3-D IMAGING WITH MICRO-BEAMFORMING THROUGH ADAPTIVE BEAMFORMING BY DEEP LEARNING

is an increased desire for miniature ultrasound probes with small apertures to provide volumet-ric images at high frame rates for in-body applications. Satisfying these increased requirements makes simulta-neous achievement of a good lateral resolution a challenge. As micro-beamforming is often employed to reduce data rate and cable count to acceptable levels, receive processing methods that try to improve spatial resolution will have to compensate the introduced reduction in focusing. Existing beamformers do not realize sufficient improvement and/or have a computational cost that prohibits their use. Here we propose the use of adaptive beamforming by deep learning (ABLE) in combination with training targets generated by a large aperture array, which inherently has better lateral resolution. In addition, we modify ABLE to extend its receptive field across multiple voxels. We illustrate that this method improves lateral resolution both quantitatively and qualitatively, such that image quality is improved compared with that achieved by existing delay-and-sum, coherence factor, filtered-delay-multiplication-and-sum and Eigen-based minimum variance beamformers. We found that only in silica data are required to train the network, making the method easily implementable in practice. (E-mail: b.w. ossenkoppele@tudelft.nl) & COPY; 2022 The Author(s). Published by Elsevier Inc. on behalf of World Federation for Ultrasound in Medicine & Biology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

There is an increased demand for miniature ultrasound probes with small apertures to provide high frame rate volumetric images for in-body applications. However, achieving a good lateral resolution remains a challenge. In this study, we propose the use of adaptive beamforming with deep learning (ABLE) in combination with training targets generated by a large aperture array to improve lateral resolution. We found that this method significantly enhances image quality compared to existing beamforming techniques.