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. 2022 Jan 12;12:619. doi: 10.1038/s41598-021-04712-8

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© The Author(s) 2022

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Super-resolution reconstruction workflow. (A) Each 1,600 frame IQ data acquisition was represented as a 3D matrix of stacked imaging frames. This was then reshaped into a Casorati matrix and an (B) SVD filter was applied to extract the moving microbubble signal from the highly spatiotemporally coherent tissue background. (C) A microbubble separation filter was then applied to split the high concentration microbubble dataset into sparser subsets, each of which were independently processed. The microbubble locations were detected on each subset using a 2D normalized cross-correlation with an empirically determined PSF function. (D) The uTrack algorithm was then applied on detected centroids to pair microbubbles and estimate trajectories. (E) The final reconstruction for this 1,600 frame dataset was produced by combining each of the independently processed data subsets. Images were rendered using MATLAB (R2019a, https://www.mathworks.com/).