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Diffuse optics (DO) is a light-based technique used to study the human brain, but it suffers from low brain specificity. Interferometric diffuse optics (iDO) promises to improve the quantitative accuracy and depth specificity of DO, and particularly, coherent light fluctuations (CLFs) arising from blood flow. iDO techniques have alternatively achieved either time-of-flight (TOF) discrimination or highly parallel detection, but not both at once. Here, we break this barrier with a single iDO instrument. Specifically, we show that rapid tuning of a temporally coherent laser during the sensor integration time increases the effective linewidth seen by a highly parallel interferometer. Using this concept to create a continuously variable and user-specified TOF filter, we demonstrate a solution to the canonical problem of DO, measuring optical properties. Then, with a deep TOF filter, we reduce scalp sensitivity of CLFs by 2.7 times at 1 cm source-collector separation. With this unique combination of desirable features, i.e., TOF-discrimination, spatial localization, and highly parallel CLF detection, we perform multiparametric imaging of light intensities and CLFs via the human forehead.

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.

Recently, we developed a time-domain diffuse correlation spectroscopy (TD-DCS) method for neurovascular sensing with higher brain sensitivity. In this paper, laser pulse shaping was designed and demonstrated for TD-DCS at 1064 nm. A quantum superconducting nanowire single-photon detector (SNSPD) with high photon detection efficiency (PDE) and low jitter collects the back-scattered light from the brain. The presented approach is the first step towards scaling up a full fiber optic cap with 96 source channels and 192 custom-made single-photon detectors which will cover most of an adult head.

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: Time domain diffuse correlation spectroscopy (TD-DCS) can offer increased sensitivity to cerebral hemodynamics and reduced contamination from extracerebral layers by differentiating photons based on their travel time in tissue. We have developed rigorous simulation and evaluation procedures to determine the optimal time gate parameters for monitoring cerebral perfusion considering instrumentation characteristics and realistic measurement noise. Aim: We simulate TD-DCS cerebral perfusion monitoring performance for different instrument response functions (IRFs) in the presence of realistic experimental noise and evaluate metrics of sensitivity to brain blood flow, signal-to-noise ratio (SNR), and ability to reject the influence of extracerebral blood flow across a variety of time gates to determine optimal operating parameters. Approach: Light propagation was modeled on an MRI-derived human head geometry using Monte Carlo simulations for 765- and 1064-nm excitation wavelengths. We use a virtual probe with a source–detector separation of 1 cm placed in the pre-frontal region. Performance metrics described above were evaluated to determine optimal time gate(s) for different IRFs. Validation of simulation noise estimates was done with experiments conducted on an intralipid-based liquid phantom. Results: We find that TD-DCS performance strongly depends on the system IRF. Among Gaussian pulse shapes, ∼300 ps pulse length appears to offer the best performance, at wide gates (500 ps and larger) with start times 400 and 600 ps after the peak of the TPSF at 765 and 1064 nm, respectively, for a 1-s integration time at photon detection rates seen experimentally (600 kcps at 765 nm and 4 Mcps at 1064 nm). Conclusions: Our work shows that optimal time gates satisfy competing requirements for sufficient sensitivity and sufficient SNR. The achievable performance is further impacted by system IRF with ∼300 ps quasi-Gaussian pulse obtained using electro-optic laser shaping providing the best results.

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.

Significance: Diffuse correlation spectroscopy (DCS) is an established optical modality that enables noninvasive measurements of blood flow in deep tissue by quantifying the temporal light intensity fluctuations generated by dynamic scattering of moving red blood cells. Compared with near-infrared spectroscopy, DCS is hampered by a limited signal-to-noise ratio (SNR) due to the need to use small detection apertures to preserve speckle contrast. However, DCS is a dynamic light scattering technique and does not rely on hemoglobin contrast; thus, there are significant SNR advantages to using longer wavelengths (>1000 nm) for the DCS measurement due to a variety of biophysical and regulatory factors. Aim: We offer a quantitative assessment of the benefits and challenges of operating DCS at 1064 nm versus the typical 765 to 850 nm wavelength through simulations and experimental demonstrations. Approach: We evaluate the photon budget, depth sensitivity, and SNR for detecting blood flow changes using numerical simulations. We discuss continuous wave (CW) and time-domain (TD) DCS hardware considerations for 1064 nm operation. We report proof-of-concept measurements in tissue-like phantoms and healthy adult volunteers. Results: DCS at 1064 nm offers higher intrinsic sensitivity to deep tissue compared with DCS measurements at the typically used wavelength range (765 to 850 nm) due to increased photon counts and a slower autocorrelation decay. These advantages are explored using simulations and are demonstrated using phantom and in vivo measurements. We show the first high-speed (cardiac pulsation-resolved), high-SNR measurements at large source–detector separation (3 cm) for CW-DCS and late temporal gates (1 ns) for TD-DCS. Conclusions: DCS at 1064 nm offers a leap forward in the ability to monitor deep tissue blood flow and could be especially useful in increasing the reliability of cerebral blood flow monitoring in adults.

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.

A fundamental challenge in non-destructive evaluation using ultrasound is to accurately estimate the thicknesses of different layers or cracks present in the object being probed. This inherently corresponds to localizing the point-sources of the reflections from the received signal. Conventional signal processing techniques cannot resolve reflectors whose spacing is below the axial resolution limit, which is of the order of the wavelength of the probing pulse. The objective of this paper is to demonstrate axial super-resolution capability using both simulated and experimental ultrasound data. We show that the ultrasound reflections could be modelled effectively as FRI signals, which can be sampled at sub-Nyquist rates. The FRI sampling method brings the reconstruction problem within a parametric estimation framework, for which efficient high-resolution spectral estimation techniques are available. We experimentally demonstrate that the proposed technique is able to resolve the thicknesses of layers of custom designed Agarose phantoms that are up to 2.25 times below the conventional resolution limit.

We demonstrate a non-invasive technique, based on the modal frequency shift of a region insonified by a dual-beam ultrasound (US) transducer (region of interest, ROI), to remotely assess the temperature of the region in a tissue-mimicking object. The application is in ultrasound hyperthermia systems for controlled maintenance of tumour temperature during chemotherapy. Towards this, we have characterised the variation of the storage modulus with the temperature of two tissue-mimicking visco-elastic materials. Due to this variation in tissue storage modulus (and viscosity), we have observed a shift in the resonant modes of the ROI, vibrated remotely with a dual-beam focussed ultrasound transducer. A modal analysis of the vibrating ROI is done to identify the modes captured by the detector. A variation in this modal frequency with temperature is computed and matches reasonably well with the experimental measurements. Through this, we demonstrate that an ultrasound hyperthermia system can have a remote temperature sensor without using an additional imaging modality.

We obtain vibro-acoustic (VA) spectral signatures of a remotely palpated region in tissue or tissue-like objects through diffusing-wave spectroscopy (DWS) measurements. Remote application of force is through focused ultrasound, and the spectral signatures correspond to vibrational modes of the focal volume (also called the ROI) excited through ultrasound forcing. In DWS, one recovers the time evolution of mean-square displacement (MSD) of Brownian particles from the measured decay of intensity autocorrelation of light, adapted also to local particles pertaining only to the ROI. We observe that the plateau of the MSD-versus-time curve has noisy fluctuations when ultrasound is applied, which disappear when forcing is removed. It is shown that the spectrum of fluctuations contains peaks corresponding to some of the modes of vibration of the ROI. This enables us to measure the vibrational modes carried by VA waves. We also show recovery of components of the orthotropic elastic tensor pertaining to the material of the ROI from the measured vibrational modes. We first recover the elastic constants for agar slabs, which are verified to be isotropic. Thereafter, we repeat the exercise on fat recovered from pork back tissue, which, from these measurements, is seen to be orthotropic. We validate some of our present measurements through independent runs in a rheometer. The present work is the first step taken, to the best of our knowledge, to characterize biological tissue on the basis of the anisotropic elasticity property, which may potentially aid in the diagnosis and tracking of the progress of cancer in soft-tissue organs.

We demonstrate a simple and computationally efficient method to recover the shear modulus pertaining to the focal volume of an ultrasound transducer from the measured vibro-acoustic spectral peaks. A model that explains the transport of local deformation information with the acoustic wave acting as a carrier is put forth. It is also shown that the peaks correspond to the natural frequencies of vibration of the focal volume, which may be readily computed by solving an eigenvalue problem associated with the vibrating region. Having measured the first natural frequency with a fibre Bragg grating sensor, and armed with an expedient means of computing the same, we demonstrate a simple procedure, based on the method of bisection, to recover the average shear modulus of the object in the ultrasound focal volume. We demonstrate this recovery for four homogeneous agarose slabs of different stiffness and verify the accuracy of the recovery using independent rheometer-based measurements. Extension of the method to anisotropic samples through the measurement of a more complete set of resonant modes and the recovery of an elasticity tensor distribution, as is done in resonant ultrasound spectroscopy, is suggested.