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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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Principle of microbubble separation based super-resolution imaging. (a) The original spatiotemporal microbubble data after tissue clutter filtering. (b) A 3D Fourier transform separates microbubble subpopulations on the basis of their speed and flow direction, resulting in datasets with sparser microbubble concentration. (c) Each data-subset undergoes independent microbubble localization and tracking. (d) The final super-resolution ULM image is generated by combining signals from all of the subset data.