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Diffusing wave spectroscopy: A unified treatment on temporal sampling and speckle ensemble methods

Xu, Jian and Jahromi, Ali K. and Yang, Changhuei (2021) Diffusing wave spectroscopy: A unified treatment on temporal sampling and speckle ensemble methods. APL Photonics, 6 (1). Art. No. 016105. ISSN 2378-0967. https://resolver.caltech.edu/CaltechAUTHORS:20210105-133437784

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Abstract

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1063/5.0034576DOIArticle
https://arxiv.org/abs/2010.13979arXivDiscussion Paper
ORCID:
AuthorORCID
Xu, Jian0000-0002-4743-2471
Jahromi, Ali K.0000-0001-9205-7853
Yang, Changhuei0000-0001-8791-0354
Additional Information:© 2021 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Submitted: 22 October 2020; Accepted: 23 December 2020; Published Online: 19 January 2021. We thank Professor Yanbei Chen for helpful discussions. This work was supported by the Rosen Bioengineering Center Endowment Fund (Grant No. 9900050). Data Availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.
Group:Rosen Bioengineering Center
Funders:
Funding AgencyGrant Number
Donna and Benjamin M. Rosen Bioengineering Center9900050
Issue or Number:1
Record Number:CaltechAUTHORS:20210105-133437784
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210105-133437784
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107330
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:06 Jan 2021 18:01
Last Modified:27 Jan 2021 18:15

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