Spectral analysis of laser speckle contrast imaging and infrared thermography to assess skin microvascular reactive hyperemia

Abstract

Background Post‐occlusive reactive hyperemia (PORH) test with signal spectral analysis coupled provides potential indicators for the assessment of microvascular functions. Objective The objective of this study is to investigate the variations of skin blood flow and temperature spectra in the PORH test. Furthermore, to quantify the oscillation amplitude response to occlusion within different frequency ranges. Materials and methods Ten healthy volunteers participated in the PORH test and their hand skin temperature and blood flow images were captured by infrared thermography (IRT) and laser speckle contrast imaging (LSCI) system, respectively. Extracted signals from selected areas were then transformed into the time‐frequency space by continuous wavelet transform for cross‐correlation analysis and oscillation amplitude response comparisons. Results The LSCI and IRT signals extracted from fingertips showed stronger hyperemia response and larger oscillation amplitude compared with other areas, and their spectral cross‐correlations decreased with frequency. According to statistical analysis, their oscillation amplitudes in the PORH stage were obviously larger than the baseline stage within endothelial, neurogenic, and myogenic frequency ranges (p < 0.05), and their quantitative indicators of oscillation amplitude response had high linear correlations within endothelial and neurogenic frequency ranges. Conclusion Comparisons of IRT and LSCI techniques in recording the reaction to the PORH test were made in both temporal and spectral domains. The larger oscillation amplitudes suggested enhanced endothelial, neurogenic, and myogenic activities in the PORH test. We hope this study is also significant for investigations of response to the PORH test by other non‐invasive techniques.

Beyond life: Exploring hemodynamic patterns in postmortem mice brains

“…Statistical analysis, including techniques like wavelet transform, and spectral analysis methods such as fast Fourier transform (FFT), enhance the interpretability of LSCI data by decomposing complex blood flow signals into simpler components [20–22]. This helps in identifying specific physiological phenomena or vascular responses, such as those occurring during reactive hyperemia.…”

Beyond Life: Exploring Hemodynamic Patterns in Postmortem Mice Brains

“…Statistical analysis, including techniques like wavelet transform, and spectral analysis methods such as Fast Fourier Transform (FFT), enhance the interpretability of LSCI data by decomposing complex blood flow signals into simpler components [20][21][22]. This helps in identifying specific physiological phenomena or vascular responses, such as those occurring during reactive hyperemia.…”