AI-assisted correlation study for quantitative diffuse speckle contrast velocimetry

Yeonhee Chang; Hanbeen Jung; Chaebeom Yeo; Cheol Song

doi:10.1117/12.3001503

13 March 2024 AI-assisted correlation study for quantitative diffuse speckle contrast velocimetry

Yeonhee Chang, Hanbeen Jung, Chaebeom Yeo, Cheol Song

Proceedings Volume PC12856, Biomedical Applications of Light Scattering XIV; PC128560A (2024) https://doi.org/10.1117/12.3001503
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Non-invasive optical blood flow monitoring systems for disease diagnosis and healthcare monitoring have been studied. Diffuse Speckle Contrast Analysis (DSCA) system can measure deep-tissue blood flow with a relatively simple system configuration, high speed, and high sensitivity. However, the relative blood flow index (BFI) is acquired with the system, and it changes with every acquisition. In this study, we adopt machine learning to overcome this limitation. DSCA system was established with a micro-size camera, and the correlation between conventional BFI and ML-based BFI was analyzed. This work will be the first step toward a quantitative Diffuse Speckle Contrast Velocimetry (DSCV).

Conference Presentation

Citation Download Citation

Yeonhee Chang, Hanbeen Jung, Chaebeom Yeo, and Cheol Song "AI-assisted correlation study for quantitative diffuse speckle contrast velocimetry", Proc. SPIE PC12856, Biomedical Applications of Light Scattering XIV, PC128560A (13 March 2024); https://doi.org/10.1117/12.3001503

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