  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.
