Improving Lateral Resolution in 3-D Imaging With Micro-beamforming Through Adaptive Beamforming by Deep Learning

Boudewine W. Ossenkoppele*, Ben Luijten, Deep Bera, Nico de Jong, Martin D. Verweij, Ruud J.G. van Sloun

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

There is an increased desire for miniature ultrasound probes with small apertures to provide volumetric images at high frame rates for in-body applications. Satisfying these increased requirements makes simultaneous 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.

Original languageEnglish
Pages (from-to)237-255
Number of pages19
JournalUltrasound in Medicine and Biology
Volume49
Issue number1
Early online date15 Oct 2022
DOIs
Publication statusPublished - 1 Jan 2023

Bibliographical note

Funding Information:
This work is part of the 3-D ICE Project (STW Project 14279), which is financed by the Netherlands Organization for Scientific Research (NWO).

Publisher Copyright:
© 2022 The Authors

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