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A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications
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A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications
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A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications
A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications
Journal Article

A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications

2024
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Overview
•We thoroughly derive and compare different layered analytical models used in continuous-wave (CW), time-domain (TD), and frequency-domain (FD)-DCS, highlighting their strengths and applications.•We discuss novel artificial intelligence (AI)-enhanced DCS analysis strategies, addressing their effectiveness and potential.•We conclude new applications of CMOS SPAD cameras and compare them with existing sensors used in DCS.•We compare TD-DCS and CW-DCS systems and emphasize the benefits of TD-DCS and its potential for future development.•The authors are leading scientists in DCS and SPAD sensors.•Discussion and outlooks are also provided. Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry to this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, sensors, and correlators), as well as data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.