Blood flow and depth measurement method based on multi-exposure laser speckle contrast imaging (MELSCI) with 3D-CNN model

Hyun-Seo Park; Yeh-Chan Ahn

doi:10.1117/12.3000819

13 March 2024 Blood flow and depth measurement method based on multi-exposure laser speckle contrast imaging (MELSCI) with 3D-CNN model

Hyun-Seo Park, Yeh-Chan Ahn

Author Affiliations +

Proceedings Volume 12856, Biomedical Applications of Light Scattering XIV; 1285607 (2024) https://doi.org/10.1117/12.3000819
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Laser speckle contrast imaging(LSCI) has been developed to measure blood perfusion non-invasively for a long time. However, there are some limitations to analyzing the random speckle phenomenon and relying on the statistical description in bio-application. This study aimed to verify the three-dimensional convolution neural network(3D-CNN) model for analyzing laser speckle images and predicting the perfusion velocity. The dataset for training deep learning was processed in the form of 3D-image and the image was from a real-time LSCI system. The model can potentially measure static and dynamic speckle information and predict perfusion velocity under the static tissue.

Citation Download Citation

Hyun-Seo Park and Yeh-Chan Ahn "Blood flow and depth measurement method based on multi-exposure laser speckle contrast imaging (MELSCI) with 3D-CNN model", Proc. SPIE 12856, Biomedical Applications of Light Scattering XIV, 1285607 (13 March 2024); https://doi.org/10.1117/12.3000819

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