High frame-rate cardiac ultrasound imaging with deep learning

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 introdu...

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Bibliographic Details
Published in:arXiv.org 2018-08
Main Authors: Ortal Senouf, Vedula, Sanketh, Zurakhov, Grigoriy, Bronstein, Alex M, Zibulevsky, Michael, Michailovich, Oleg, Adam, Dan, Blondheim, David
Format: Article
Language:English
Subjects:
Artificial neural networks
Data acquisition
Deep learning
Digital cameras
Image acquisition
Image quality
Neural networks
Ultrasonic imaging
Online Access:Get full text
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Summary: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.
ISSN:2331-8422