First-in-clinical application of a time-gated diffuse correlation spectroscopy system at 1064 nm using superconducting nanowire single photon detectors in a neuro intensive care unit.

Recently proposed time-gated diffuse correlation spectroscopy (TG-DCS) has significant advantages compared to conventional continuous wave (CW)-DCS, but it is still in an early stage and clinical capability has yet to be established. The main challenge for TG-DCS is the lower signal-to-noise ratio (SNR) when gating for the deeper traveling late photons. Longer wavelengths, such as 1064 nm have a smaller effective attenuation coefficient and a higher power threshold in humans, which significantly increases the SNR.

Noninvasive Optical Monitoring of Cerebral Blood Flow and EEG Spectral Responses after Severe Traumatic Brain Injury: A Case Report.

Survivors of severe brain injury may require care in a neurointensive care unit (neuro-ICU), where the brain is vulnerable to secondary brain injury. Thus, there is a need for noninvasive, bedside, continuous cerebral blood flow monitoring approaches in the neuro-ICU. Our goal is to address this need through combined measurements of EEG and functional optical spectroscopy (EEG-Optical) instrumentation and analysis to provide a complementary fusion of data about brain activity and function.

Quantification of perfusion and metabolism in an autism mouse model assessed by diffuse correlation spectroscopy and near-infrared spectroscopy.

There is a need for quantitative biomarkers for early diagnosis of autism. Cerebral blood flow and oxidative metabolism parameters may show superior contrasts for improved characterization. Diffuse correlation spectroscopy (DCS) has been shown to be reliable method to obtain cerebral blood flow contrast in animals and humans.

Deep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy.

Diffuse correlation spectroscopy (DCS) is increasingly used in the optical imaging field to assess blood flow in humans due to its non-invasive, real-time characteristics and its ability to provide label-free, bedside monitoring of blood flow changes. Previous DCS studies have utilized a traditional curve fitting of the analytical or Monte Carlo models to extract the blood flow changes, which are computationally demanding and less accurate when the signal to noise ratio decreases. Here, we present a deep learning model that eliminates this bottleneck by solving the inverse problem more than 2300% faster, with equivalent or improved accuracy compared to the nonlinear fitting with an analytical method.

Analytical models for time-domain diffuse correlation spectroscopy for multi-layer and heterogeneous turbid media.

A novel approach for time-domain diffuse correlation spectroscopy (TD-DCS) has been recently proposed, which has the unique advantage by simultaneous measurements of optical and dynamical properties in a scattering medium. In this study, analytical models for calculating the time-resolved electric-field autocorrelation function is presented for a multi-layer turbid sample, as well as a semi-infinite medium embedded with a small dynamic heterogeneity. To verify the analytical models, we used Monte Carlo simulations, which demonstrated that the theoretical prediction for the time-resolved autocorrelation function was highly consistent with the Monte Carlo simulation, validating the proposed analytical models.

Resting-state functional connectivity measured by diffuse correlation spectroscopy.

Near-infrared diffuse correlation spectroscopy (DCS) is used to record spontaneous cerebral blood flow fluctuations in the frontal cortex. Nine adult subjects participated in the experiments, in which 8-minute spontaneous fluctuations were simultaneously recorded from the left and right dorsolateral and inferior frontal regions. Resting-state functional connectivity (RSFC) was measured by the temporal correlation of the low frequency fluctuations.