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Significance: The ability of diffuse correlation spectroscopy (DCS) to measure cerebral blood flow (CBF) in humans is hindered by the low signal-to-noise ratio (SNR) of the method. This limits the high acquisition rates needed to resolve dynamic flow changes and to optimally filter out large pulsatile oscillations and prevents the use of large source-detector separations (≥3 cm), which are needed to achieve adequate brain sensitivity in most adult subjects. Aim: To substantially improve SNR, we have built a DCS device that operates at 1064 nm and uses superconducting nanowire single-photon detectors (SNSPD). Approach: We compared the performances of the SNSPD-DCS in humans with respect to a typical DCS system operating at 850 nm and using silicon single-photon avalanche diode detectors. Results: At a 25-mm separation, we detected 13 ± 6 times more photons and achieved an SNR gain of 16 ± 8 on the forehead of 11 subjects using the SNSPD-DCS as compared to typical DCS. At this separation, the SNSPD-DCS is able to detect a clean pulsatile flow signal at 20 Hz in all subjects. With the SNSPD-DCS, we also performed measurements at 35 mm, showing a lower scalp sensitivity of 31 ± 6 % with respect to the 48 ± 8 % scalp sensitivity at 25 mm for both the 850 and 1064 nm systems. Furthermore, we demonstrated blood flow responses to breath holding and hyperventilation tasks. Conclusions: While current commercial SNSPDs are expensive, bulky, and loud, they may allow for more robust measures of non-invasive cerebral perfusion in an intensive care setting.

Non-invasive monitoring of cerebral blood flow at the bedside using diffuse correlation spectroscopy is being investigated as a potential tool to improve brain health outcomes after surgery. In this work we characterize the performance of diffuse correlation spectroscopy measurements in assessing cerebral blood flow in the presence of systemic physiology interference through measurements on several healthy volunteers during CO2 inhalation. We report group averaged responses and the role of multi-layer models in increasing the accuracy of CBF estimates. We compare optical blood flow recordings with transcranial Doppler ultrasound and MRI ASL data.

The ability of diffuse correlation spectroscopy (DCS) to measure tissue perfusion paves the way for monitoring cerebral blood flow (CBF) non-invasively. However, during measurements on human forehead, the measured blood flow index (BFi) is susceptible to contamination due to the blood flow in extracerebral tissue. Time domain DCS addresses this problem by selecting photons based on their travel time to obtain BFi at various depths. We have determined the gate start time(s) and width(s) that can lead to optimal sensitivity of BFi to CBF during actual measurements on human subjects through simulations. The simulated parameters were compared with measurement data.

Significance: Contamination of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow (CBF) due to systemic physiology remains a significant challenge in the clinical translation of DCS for neuromonitoring. Tunable, multi-layer Monte Carlo-based (MC) light transport models have the potential to remove extracerebral flow cross-talk in cerebral blood flow index (CBFi) estimates. Aim: We explore the effectiveness of MC DCS models in recovering accurate CBFi changes in the presence of strong systemic physiology variations during a hypercapnia maneuver. Approach: Multi-layer slab and head-like realistic (curved) geometries were used to run MC simulations of photon propagation through the head. The simulation data were post-processed into models with variable extracerebral thicknesses and used to fit DCS multi-distance intensity autocorrelation measurements to estimate CBFi timecourses. The results of the MC CBFi values from a set of human subject hypercapnia sessions were compared with CBFi values estimated using a semi-infinite analytical model, as commonly used in the field. Results: Group averages indicate a gradual systemic increase in blood flow following a different temporal profile versus the expected rapid CBF response. Optimized MC models, guided by several intrinsic criteria and a pressure modulation maneuver, were able to more effectively separate CBFi changes from scalp blood flow influence than the analytical fitting, which assumed a homogeneous medium. Three-layer models performed better than two-layer ones; slab and curved models achieved largely similar results, though curved geometries were closer to physiological layer thicknesses. Conclusion: Three-layer, adjustable MC models can be useful in separating distinct changes in scalp and brain blood flow. Pressure modulation, along with reasonable estimates of physiological parameters, can help direct the choice of appropriate layer thicknesses in MC models.

In this paper, we demonstrated a superconductive sensing-based system for cerebral blood flow monitoring via diffuse correlation spectroscopy at 1064nm. Findings show high brain sensitivity and fast monitoring using the presented system.

Diffuse correlation spectroscopy (DCS) is an increasingly widespread non-invasive technology to measure tissue perfusion. Extending this technique into adult brain monitoring to assess real-time cerebral blood flow (CBF) requires addressing the influence of extracerebral contributions on DCS measurements. We compare several Monte Carlo based forward simulation models on the efficacy of CBF isolation, including ones generated directly from individual subject MRI scans. We conclude that a multi-layer curved surface representation is beneficial, and that the traditional single-layer homogenous model is insufficient; however, detailed structural information such as cortical folding represented in an individualized tissue-specific model may not be needed.

The ability of diffuse correlation spectroscopy (DCS) to measure tissue perfusion paves the way for monitoring cerebral blood flow non-invasively. However, during measurements on human forehead, the measured blood flow index (BFi) is susceptible to contamination due to the blood flow in extracerebral tissue. Time domain DCS addresses this problem by selecting photons based on their travel time to obtain BFi at various depths. We have determined the gate start time(s) and width(s) that can lead to optimal sensitivity of BFi to brain blood flow during actual measurements on human subjects using commercially available hardware with accurate noise modelling.

Time-domain diffuse correlation spectroscopy (TD-DCS) aims to increase cerebral blood flow (CBF) sensitivity by discriminating photon time of flight. We report on the optimization of the laser pulse shape to maximize TD-DCS performance.