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Erschienen in:
2018 | OriginalPaper | Buchkapitel
High Frame-Rate Cardiac Ultrasound Imaging with Deep Learning
verfasst von : Ortal Senouf, Sanketh Vedula, Grigoriy Zurakhov, Alex Bronstein, Michael Zibulevsky, Oleg Michailovich, Dan Adam, David Blondheim
Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
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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.