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

TL;DR

This paper introduces interferometric speckle visibility spectroscopy (ISVS), a novel non-invasive method that significantly improves the detection of cerebral blood flow signals in deep tissue by boosting weak photon signals using interferometry.

Contribution

The paper presents ISVS, a new interferometric detection scheme that enhances blood flow monitoring in deep tissue with low photon counts, achieving high SNR and real-time measurement capabilities.

Findings

ISVS improves measurement fidelity with low photon counts.

The system operates at 100 Hz sampling rate.

Successfully monitors human cerebral blood flow and changes during breath holding.

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 of measurement fidelity by introducing interferometric detection when the signal photon number is insufficient. We apply the ISVS system to monitor the human CBF in situations where the light intensity is $\sim$100-fold less than that in common diffuse correlation spectroscopy (DCS) implementations. Due to the large number of pixels ($\sim 2\times 10^5$) 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 photon electron number is $\sim$0.95 count/pixel, yielding a single pixel interferometric measurement signal-to-noise ratio (SNR) of $\sim$0.97. The total $\sim 2\times 10^5$ 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.