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

TL;DR

This paper provides a comprehensive review of diffuse correlation spectroscopy (DCS), covering its theoretical foundations, hardware advancements, data analysis techniques, and diverse applications, aiming to guide newcomers in the field.

Contribution

It offers an extensive overview of DCS hardware architectures, models, and recent technological advances, including new sensors and analysis methods, to facilitate entry and progress in DCS research.

Findings

Comparison of SPAD sensors with existing options

Summary of recent hardware and analysis advancements

Discussion of future directions and applications

Abstract

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 into 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, fibers, and correlators), as well as new 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.