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. 2019 Mar 20;9:4907. doi: 10.1038/s41598-019-41373-0

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© The Author(s) 2019

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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SVD-based clutter filtering of Vevo^® 3100 cineloops demonstrated superior microvascular detection and reduced tissue background. (A) B-mode IQ cineloops (100 frames) were considered as three-dimensional imaging volumes with axial, lateral, and slow-time dimensions. (B) These spatiotemporal data sets were reshaped into 2D Casorati matrices in a column-wise manner. Singular value decomposition of Casorati matrices decomposed the data into singular vectors and singular values, allowing for the extraction of blood signals. (C) An inverse SVD was performed to recover blood flow signals back to the spatiotemporal domain, resulting in a superior microvascular image in comparison to conventional power Doppler mode.