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Updated: Jan 14, 2026
05:41
Non-invasive Assessment of Microvascular and Endothelial Function
Published on: January 29, 2013
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Optical Techniques to Assess Cutaneous Microvascular Function in Cardiovascular Disease.
IEEE Reviews in Biomedical Engineering
|January 12, 2026
Summary
Optical techniques noninvasively assess skin microcirculation, offering insights into systemic vascular health. This review details various light-based methods and their clinical relevance for understanding microvascular function.
Area of Science:
- Physiology
- Biomedical Engineering
- Optical Imaging
Background:
- Microcirculation is vital for tissue health and systemic physiological function.
- Cutaneous microvasculature provides accessible, noninvasive insights into systemic vascular conditions.
- Existing reviews lack a unified focus on optical techniques for microcirculation or their clinical evidence.
Purpose of the Study:
- To systematically review optical technologies for assessing cutaneous microvascular function.
- To bridge the gap between technological innovation and clinical evidence in microcirculation research.
- To evaluate the clinical relevance and technical maturity of various optical techniques.
Main Methods:
- Review of non-coherent light techniques (oximetry, photoplethysmography, microscopy).
- Review of coherent light techniques (speckle contrast imaging, diffuse correlation spectroscopy, photoacoustic imaging, laser Doppler flowmetry, self-mixing interferometry).
- Discussion of skin structure, microvasculature, and light-skin interactions.
Main Results:
- Comprehensive overview of diverse optical techniques for microvascular assessment.
- Evaluation of techniques' applicability in cardiovascular research.
- Analysis of clinical relevance and technical maturity of reviewed methods.
Conclusions:
- Optical techniques offer powerful noninvasive tools for evaluating cutaneous microvascular function.
- These methods provide valuable insights into systemic vascular health and disease.
- Future directions include multimodal monitoring and machine learning integration.
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