[{"doi":"10.1007/978-3-030-00129-2_17","conference":{"end_date":"2018-09-16","name":"MLMIR: Workshop on Machine Learning for Medical Image Reconstruction","start_date":"2018-09-16","location":"Granada, Spain"},"alternative_title":["LNCS"],"article_processing_charge":"No","volume":11074,"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","title":"High quality ultrasonic multi-line transmission through deep learning","_id":"18284","day":"12","page":"147 - 155","publication_identifier":{"issn":["0302-9743"],"eissn":["1611-3349"],"isbn":["9783030001285"],"eisbn":["9783030001292"]},"fulldoi":"https://doi.org/10.1007/978-3-030-00129-2_17","publication_status":"published","publication":"First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018","year":"2018","date_created":"2024-10-09T07:48:06Z","intvolume":"     11074","date_published":"2018-09-12T00:00:00Z","publisher":"Springer Nature","month":"09","extern":"1","oa_version":"None","citation":{"ieee":"S. Vedula <i>et al.</i>, “High quality ultrasonic multi-line transmission through deep learning,” in <i>First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018</i>, Granada, Spain, 2018, vol. 11074, pp. 147–155.","ista":"Vedula S, Senouf O, Zurakhov G, Bronstein AM, Zibulevsky M, Michailovich O, Adam D, Gaitini D. 2018. High quality ultrasonic multi-line transmission through deep learning. First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018. MLMIR: Workshop on Machine Learning for Medical Image Reconstruction, LNCS, vol. 11074, 147–155.","ama":"Vedula S, Senouf O, Zurakhov G, et al. High quality ultrasonic multi-line transmission through deep learning. In: <i>First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018</i>. Vol 11074. Springer Nature; 2018:147-155. doi:<a href=\"https://doi.org/10.1007/978-3-030-00129-2_17\">10.1007/978-3-030-00129-2_17</a>","chicago":"Vedula, Sanketh, Ortal Senouf, Grigoriy Zurakhov, Alex M. Bronstein, Michael Zibulevsky, Oleg Michailovich, Dan Adam, and Diana Gaitini. “High Quality Ultrasonic Multi-Line Transmission through Deep Learning.” In <i>First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018</i>, 11074:147–55. Springer Nature, 2018. <a href=\"https://doi.org/10.1007/978-3-030-00129-2_17\">https://doi.org/10.1007/978-3-030-00129-2_17</a>.","mla":"Vedula, Sanketh, et al. “High Quality Ultrasonic Multi-Line Transmission through Deep Learning.” <i>First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018</i>, vol. 11074, Springer Nature, 2018, pp. 147–55, doi:<a href=\"https://doi.org/10.1007/978-3-030-00129-2_17\">10.1007/978-3-030-00129-2_17</a>.","apa":"Vedula, S., Senouf, O., Zurakhov, G., Bronstein, A. M., Zibulevsky, M., Michailovich, O., … Gaitini, D. (2018). High quality ultrasonic multi-line transmission through deep learning. In <i>First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018</i> (Vol. 11074, pp. 147–155). Granada, Spain: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-030-00129-2_17\">https://doi.org/10.1007/978-3-030-00129-2_17</a>","short":"S. Vedula, O. Senouf, G. Zurakhov, A.M. Bronstein, M. Zibulevsky, O. Michailovich, D. Adam, D. Gaitini, in:, First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Springer Nature, 2018, pp. 147–155."},"quality_controlled":"1","status":"public","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"Frame rate is a crucial consideration in cardiac ultrasound imaging and 3D sonography. Several methods have been proposed in the medical ultrasound literature aiming at accelerating the image acquisition. In this paper, we consider one such method called multi-line transmission (MLT), in which several evenly separated focused beams are transmitted simultaneously. While MLT reduces the acquisition time, it comes at the expense of a heavy loss of contrast due to the interactions between the beams (cross-talk artifact). In this paper, we introduce a data-driven method to reduce the artifacts arising in MLT. To this end, we propose to train an end-to-end convolutional neural network consisting of correction layers followed by a constant apodization layer. The network is trained on pairs of raw data obtained through MLT and the corresponding single-line transmission (SLT) data. Experimental evaluation demonstrates significant improvement both in the visual image quality and in objective measures such as contrast ratio and contrast-to-noise ratio, while preserving resolution unlike traditional apodization-based methods. We show that the proposed method is able to generalize well across different patients and anatomies on real and phantom data."}],"type":"conference","author":[{"first_name":"Sanketh","last_name":"Vedula","full_name":"Vedula, Sanketh"},{"full_name":"Senouf, Ortal","last_name":"Senouf","first_name":"Ortal"},{"full_name":"Zurakhov, Grigoriy","first_name":"Grigoriy","last_name":"Zurakhov"},{"orcid":"0000-0001-9699-8730","full_name":"Bronstein, Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","last_name":"Bronstein","first_name":"Alexander"},{"full_name":"Zibulevsky, Michael","last_name":"Zibulevsky","first_name":"Michael"},{"last_name":"Michailovich","first_name":"Oleg","full_name":"Michailovich, Oleg"},{"first_name":"Dan","last_name":"Adam","full_name":"Adam, Dan"},{"full_name":"Gaitini, Diana","last_name":"Gaitini","first_name":"Diana"}],"scopus_import":"1","date_updated":"2025-01-23T12:53:22Z"}]