High frame-rate cardiac ultrasound imaging with deep learning

Ortal Senouf; Sanketh Vedula; Grigoriy Zurakhov; Alex M. Bronstein; Michael Zibulevsky; Oleg Michailovich; Dan Adam; David Blondheim

arXiv · 1808.07823

High frame-rate cardiac ultrasound imaging with deep learning

Ortal Senouf·Sanketh Vedula·Grigoriy Zurakhov·Alex M. Bronstein·Michael Zibulevsky·Oleg Michailovich·Dan Adam·David Blondheim

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Abstract

Cardiac ultrasound imaging requires a high frame rate in order to capture rapid motion. This can be achieved by multi-line acquisition (MLA), where several narrow-focused received lines are obtained from each wide-focused transmitted line. This shortens the acquisition time at the expense of introducing block artifacts. In this paper, we propose a data-driven learning-based approach to improve the MLA image quality. We train an end-to-end convolutional neural network on pairs of real ultrasound cardiac data, acquired through MLA and the corresponding single-line acquisition (SLA). The network achieves a significant improvement in image quality for both $5-$ and $7-$line MLA resulting in a decorrelation measure similar to that of SLA while having the frame rate of MLA.

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Ortal Senouf, Sanketh Vedula, Grigoriy Zurakhov, Alex M. Bronstein, Michael Zibulevsky, Oleg Michailovich, Dan Adam, David Blondheim. 2018-08-23. High frame-rate cardiac ultrasound imaging with deep learning. https://arxiv.org/abs/1808.07823

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