High Frame-Rate Cardiac Ultrasound Imaging with Deep Learning

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

doi:10.1007/978-3-030-00928-1_15

High Frame-Rate Cardiac Ultrasound Imaging with Deep Learning

Ortal Senouf, David Blondheim + Show 6 more

Open Access

https://doi.org/10.1007/978-3-030-00928-1_15

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Publication Date: Jan 1, 2018

Citations: 33

Affiliation: Technion – Israel Institute of Technology, Hillel Yaffe Medical Center, University of Waterloo

#Multi-line Acquisition #Cardiac Ultrasound Imaging + Show 8 more

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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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