{"quality_controlled":"1","author":[{"full_name":"Senouf, Ortal","last_name":"Senouf","first_name":"Ortal"},{"full_name":"Vedula, Sanketh","first_name":"Sanketh","last_name":"Vedula"},{"first_name":"Grigoriy","last_name":"Zurakhov","full_name":"Zurakhov, Grigoriy"},{"id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander","last_name":"Bronstein","first_name":"Alexander","orcid":"0000-0001-9699-8730"},{"last_name":"Zibulevsky","first_name":"Michael","full_name":"Zibulevsky, Michael"},{"full_name":"Michailovich, Oleg","last_name":"Michailovich","first_name":"Oleg"},{"full_name":"Adam, Dan","last_name":"Adam","first_name":"Dan"},{"full_name":"Blondheim, David","first_name":"David","last_name":"Blondheim"}],"month":"09","article_processing_charge":"No","scopus_import":"1","doi":"10.1007/978-3-030-00928-1_15","year":"2018","oa_version":"None","extern":"1","issue":"Part 1","alternative_title":["LNCS"],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publication_status":"published","date_published":"2018-09-14T00:00:00Z","citation":{"ista":"Senouf O, Vedula S, Zurakhov G, Bronstein AM, Zibulevsky M, Michailovich O, Adam D, Blondheim D. 2018. High frame-rate cardiac ultrasound imaging with deep learning. International Conference on Medical Image Computing and Computer Assisted Intervention. MICCAI: Medical Image Computing and Computer Assisted Intervention, LNCS, vol. 11070, 126–134.","apa":"Senouf, O., Vedula, S., Zurakhov, G., Bronstein, A. M., Zibulevsky, M., Michailovich, O., … Blondheim, D. (2018). High frame-rate cardiac ultrasound imaging with deep learning. In <i>International Conference on Medical Image Computing and Computer Assisted Intervention</i> (Vol. 11070, pp. 126–134). Granada, Spain: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-00928-1_15\">https://doi.org/10.1007/978-3-030-00928-1_15</a>","ama":"Senouf O, Vedula S, Zurakhov G, et al. High frame-rate cardiac ultrasound imaging with deep learning. In: <i>International Conference on Medical Image Computing and Computer Assisted Intervention</i>. Vol 11070. Springer Nature; 2018:126-134. doi:<a href=\"https://doi.org/10.1007/978-3-030-00928-1_15\">10.1007/978-3-030-00928-1_15</a>","mla":"Senouf, Ortal, et al. “High Frame-Rate Cardiac Ultrasound Imaging with Deep Learning.” <i>International Conference on Medical Image Computing and Computer Assisted Intervention</i>, vol. 11070, no. Part 1, Springer Nature, 2018, pp. 126–34, doi:<a href=\"https://doi.org/10.1007/978-3-030-00928-1_15\">10.1007/978-3-030-00928-1_15</a>.","chicago":"Senouf, Ortal, Sanketh Vedula, Grigoriy Zurakhov, Alex M. Bronstein, Michael Zibulevsky, Oleg Michailovich, Dan Adam, and David Blondheim. “High Frame-Rate Cardiac Ultrasound Imaging with Deep Learning.” In <i>International Conference on Medical Image Computing and Computer Assisted Intervention</i>, 11070:126–34. Springer Nature, 2018. <a href=\"https://doi.org/10.1007/978-3-030-00928-1_15\">https://doi.org/10.1007/978-3-030-00928-1_15</a>.","short":"O. Senouf, S. Vedula, G. Zurakhov, A.M. Bronstein, M. Zibulevsky, O. Michailovich, D. Adam, D. Blondheim, in:, International Conference on Medical Image Computing and Computer Assisted Intervention, Springer Nature, 2018, pp. 126–134.","ieee":"O. Senouf <i>et al.</i>, “High frame-rate cardiac ultrasound imaging with deep learning,” in <i>International Conference on Medical Image Computing and Computer Assisted Intervention</i>, Granada, Spain, 2018, vol. 11070, no. Part 1, pp. 126–134."},"publication":"International Conference on Medical Image Computing and Computer Assisted Intervention","publisher":"Springer Nature","abstract":[{"lang":"eng","text":"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."}],"_id":"18283","type":"conference","conference":{"start_date":"2018-09-16","end_date":"2018-09-20","name":"MICCAI: Medical Image Computing and Computer Assisted Intervention","location":"Granada, Spain"},"publication_identifier":{"isbn":["9783030009274"],"issn":["0302-9743"],"eissn":["1611-3349"],"eisbn":["9783030009281"]},"date_created":"2024-10-09T07:47:49Z","date_updated":"2025-01-23T13:13:01Z","intvolume":"     11070","volume":11070,"status":"public","title":"High frame-rate cardiac ultrasound imaging with deep learning","day":"14","page":"126 - 134","language":[{"iso":"eng"}]}