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. 2020 Apr 7;10:6007. doi: 10.1038/s41598-020-62898-9

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Flow channel phantom validates the proposed method. (a) Reference ULM intensity and velocity images of the flow channel phantom that were generated by combining eight individually processed ULM images from separate acquisitions at a low microbubble concentration (a total of four different flow speeds and two different flow directions). (b) The combined ULM flow channel image demonstrated a well-defined parabolic flow profile. (c) Conventional ULM image processing performed on a high microbubble concentration dataset did not result in an adequate number of high-confidence microbubble events to populate the vessel lumen and (d) did not produce a meaningful flow profile. (e) Performing microbubble separation on the high microbubble concentration dataset allowed for accurate reconstruction and filling of the vessel lumen and (f) could recover a parabolic flow profile.