3-D contrast enhanced ultrasound imaging of an in vivo chicken embryo with a sparse array and deep learning based adaptive beamforming

Boudewine W. Ossenkoppele, Luxi Wei, Ben Luijten, Hendrik J. Vos, Nico De Jong, Ruud J.G. Van Sloun, Martin D. Verweij

Research output: Contribution to journalConference articleAcademicpeer-review

1 Citation (Scopus)

Abstract

3-D contrast enhanced ultrasound enables better visualization of inherently 3-D vascular geometries compared to an intersecting plane. Additionally, it would allow the application of motion correction techniques for all directions. Both contrast detection and motion correction work better on high-frame rate data. However high-frame rate 3-D ultrasound imaging with dense matrix arrays is challenging to realize. Sparse arrays alleviate some of the limitations in cable count and data rate that fully populated arrays encounter, but their increased level of secondary lobes negatively impacts image contrast. Meanwhile the use of unfocused transmit beams needed to achieve high-frame rates negatively impacts resolution. Here we propose to use adaptive beamforming by deep learning (ABLE) to improve the image quality of contrast enhanced ultrasound images acquired with a sparse spiral array. We train the neural network on simulated data and evaluate simulated images and in vivo images of an ex ovo chicken embryo. ABLE improved resolution compared to delay-and-sum (DAS) and spatial coherence (SC) beamforming on the simulated and in vivo data. The qualitative improvements persist after histogram matching, indicating that the image quality improvement of the ABLE images was not purely due to dynamic range stretching.

Original languageEnglish
Pages (from-to)1-4
JournalIEEE International Ultrasonics Symposium, IUS
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Ultrasonics Symposium, IUS 2022 - Venice, Italy
Duration: 10 Oct 202213 Oct 2022

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 IEEE.

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