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Diffusing wave spectroscopy (DWS) is a well-known set of methods to measure the temporal dynamics of dynamic samples. In DWS, dynamic samples scatter the incident coherent light, and the information of the temporal dynamics is encoded in the scattered light. To record and analyze the light signal, there exist two types of methods—temporal sampling methods and speckle ensemble methods. Temporal sampling methods, including diffuse correlation spectroscopy, use one or multiple large bandwidth detectors to sample well and analyze the temporal light signal to infer the sample temporal dynamics. Speckle ensemble methods, including speckle visibility spectroscopy, use a high-pixel-count camera sensor to capture a speckle pattern and use the speckle contrast to infer sample temporal dynamics. In this paper, we theoretically and experimentally demonstrate that the decorrelation time (τ) measurement accuracy or signal-to-noise ratio (SNR) of the two types of methods has a unified and similar fundamental expression based on the number of independent observables (NIO) and the photon flux. Given a time measurement duration, the NIO in temporal sampling methods is constrained by the measurement duration, while speckle ensemble methods can outperform by using simultaneous sampling channels to scale up the NIO significantly. In the case of optical brain monitoring, the interplay of these factors favors speckle ensemble methods. We illustrate that this important engineering consideration is consistent with the previous research on blood pulsatile flow measurements, where a speckle ensemble method operating at 100-fold lower photon flux than a conventional temporal sampling system can achieve a comparable SNR. [ABSTRACT FROM AUTHOR]

Infrared light scattering methods have been developed and employed to non-invasively monitor human cerebral blood flow (CBF). However, the number of reflected photons that interact with the brain is low when detecting blood flow in deep tissue. To tackle this photon-starved problem, we present and demonstrate the idea of interferometric speckle visibility spectroscopy (ISVS). In ISVS, an interferometric detection scheme is used to boost the weak signal light. The blood flow dynamics are inferred from the speckle statistics of a single frame speckle pattern. We experimentally demonstrated the improvement in the measurement of fidelity by introducing interferometric detection when the signal photon number is low. We apply the ISVS system to monitor the human CBF in situations where the light intensity is ∼100-fold less than that in common diffuse correlation spectroscopy (DCS) implementations. Due to the large number of pixels (∼2 × 105) used to capture light in the ISVS system, we are able to collect a similar number of photons within one exposure time as in normal DCS implementations. Our system operates at a sampling rate of 100 Hz. At the exposure time of 2 ms, the average signal photoelectron number is ∼0.95 count/pixel, yielding a single pixel interferometric measurement signal-to-noise ratio (SNR) of ∼0.97. The total ∼2 × 105 pixels provide an expected overall SNR of 436. We successfully demonstrate that the ISVS system is able to monitor the human brain pulsatile blood flow, as well as the blood flow change when a human subject is doing a breath-holding task. [ABSTRACT FROM AUTHOR]

REFERENCE 1 J. Xu, A. K. Jahromi, and C. Yang, "Diffusing wave spectroscopy: A unified treatment on temporal sampling and speckle ensemble methods", APL Photonics 6, 016105 (2021).10.1063/5.0034576 The original article[1] contains misprints in Eq. (A23) and the first line of Eq. (A24). [Extracted from the article]