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Interferometric speckle visibility spectroscopy (ISVS) for human cerebral blood flow monitoring

Xu, Jian and Jahromi, Ali K. and Brake, Joshua and Robinson, J. Elliott and Yang, Changhuei (2020) Interferometric speckle visibility spectroscopy (ISVS) for human cerebral blood flow monitoring. APL Photonics, 5 (12). Art. No. 126102. ISSN 2378-0967. https://resolver.caltech.edu/CaltechAUTHORS:20201208-105038376

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Abstract

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1063/5.0021988DOIArticle
ORCID:
AuthorORCID
Xu, Jian0000-0002-6036-5680
Jahromi, Ali K.0000-0001-9205-7853
Brake, Joshua0000-0002-5113-6886
Robinson, J. Elliott0000-0001-9417-3938
Yang, Changhuei0000-0001-8791-0354
Additional Information:© 2020 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: 16 July 2020 • Accepted: 19 November 2020 • Published Online: 4 December 2020 The authors would like to thank Professor John O’Doherty, Professor Adoph Ralphs, Dr. Yan Liu, Dr. Haowen Ruan, and Dr. Haojiang Zhou for their helpful discussions. This research was supported by Kernel-Brain Research and Technologies—Grant No. FS 13520230. AUTHORS’ CONTRIBUTIONS. J.X., A.K.J., and J.B. contributed equally to this work. DATA AVAILABILITY. The data that support the findings of this study are available from the corresponding author upon reasonable request.
Funders:
Funding AgencyGrant Number
Kernel-Brain Research and TechnologiesFS 13520230
Issue or Number:12
Record Number:CaltechAUTHORS:20201208-105038376
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201208-105038376
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106963
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:09 Dec 2020 15:21
Last Modified:09 Dec 2020 15:21

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