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. 2023 Sep 16;13:15377. doi: 10.1038/s41598-023-41846-3

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© The Author(s) 2023

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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Schematic depictions of two D-OCT algorithms:(a) LIV and (b) OCDS $_{l}$ . LIV is defined as the temporal fluctuation (variance) of the dB-scaled OCT intensity. As a result, LIV is expected to be sensitive to the magnitude of the tissue dynamics. On the other hand, OCDS $_{l}$ represents the speed of the dynamics and is defined as the decorrelation rate, i.e., as the slope of the auto-correlation curve [red curve in (b)] of the dB-scaled OCT intensity. The slope is computed here by performing a linear regression of the auto-correlation curve over a specified delay-time range [204.8, 1,228.8 ms].