Diffuse correlation spectroscopy measurements of blood flow using 1064 nm light

1060 nm interferometric near-infrared spectroscopy

Recently, interferometric near-infrared spectroscopy (iNIRS) has emerged to measure diffuse light field fluctuations with time-of-flight (TOF) resolution. Yet, current iNIRS implementations suffer from low signal-to-noise ratio (SNR). Longer wavelengths, with lower photon energy, lower reduced scattering in biological tissues, and higher permissible exposures, have the potential to increase SNR. Here, we investigate iNIRS at 1060 nm. Across various forehead locations, we find that the autocorrelation SNR is improved 3.7-9.3 times compared to 855 nm and 6.0-33.5 times compared to 773 nm at TOFs of 800-1000 ps. Physical system parameters account for much of this improvement, but the tissue response may also play a role. We conclude that wavelengths near 1060 nm can potentially improve iNIRS measurements of TOF-resolved speckle fluctuations.

Time-Resolved Laser Speckle Contrast Imaging (TR-LSCI) of Cerebral Blood Flow

To address many of the deficiencies in optical neuroimaging technologies, such as poor tempo-spatial resolution, low penetration depth, contact-based measurement, and time-consuming image reconstruction, a novel, noncontact, portable, time-resolved laser speckle contrast imaging (TR-LSCI) technique has been developed for continuous, fast, and high-resolution 2D mapping of cerebral blood flow (CBF) at different depths of the head. TR-LSCI illuminates the head with picosecond-pulsed, coherent, widefield near-infrared light and synchronizes a fast, high-resolution, gated single-photon avalanche diode camera to selectively collect diffuse photons with longer pathlengths through the head, thus improving the accuracy of CBF measurement in the deep brain. The reconstruction of a CBF map was dramatically expedited by incorporating convolution functions with parallel computations. The performance of TR-LSCI was evaluated using head-simulating phantoms with known properties and in-vivo rodents with varied hemodynamic challenges to the brain. TR-LSCI enabled mapping CBF variations at different depths with a sampling rate of up to 1 Hz and spatial resolutions ranging from tens/hundreds of micrometers on rodent head surfaces to 1-2 millimeters in deep brains. With additional improvements and validation in larger populations against established methods, we anticipate offering a noncontact, fast, high-resolution, portable, and affordable brain imager for fundamental neuroscience research in animals and for translational studies in humans.

Programmable scanning diffuse speckle contrast imaging of cerebral blood flow

Significance

Cerebral blood flow (CBF) imaging is crucial for diagnosing cerebrovascular diseases. However, existing large neuroimaging techniques with high cost, low sampling rate, and poor mobility make them unsuitable for continuous and longitudinal CBF monitoring at the bedside.

Aim

We aimed to develop a low-cost, portable, programmable scanning diffuse speckle contrast imaging (PS-DSCI) technology for fast, high-density, and depth-sensitive imaging of CBF in rodents.

Approach

The PS-DSCI employed a programmable digital micromirror device (DMD) for remote line-shaped laser (785 nm) scanning on tissue surface and synchronized a 2D camera for capturing boundary diffuse laser speckle contrasts. New algorithms were developed to address deformations of line-shaped scanning, thus minimizing CBF reconstruction artifacts. The PS-DSCI was examined in head-simulating phantoms and adult mice.

Results

The PS-DSCI enables resolving intralipid particle flow contrasts at different tissue depths. In vivo experiments in adult mice demonstrated the capability of PS-DSCI to image global/regional CBF variations induced by 8%CO 2 inhalation and transient carotid artery ligations.

Conclusions

Compared with conventional point scanning, line scanning in PS-DSCI significantly increases spatiotemporal resolution. The high sampling rate of PS-DSCI is crucial for capturing rapid CBF changes while high spatial resolution is important for visualizing brain vasculature.

A comparison between 64-projection and 32-projection myocardial perfusion scintigraphy

Aims

While most clinical guidelines recommend using a 64-projection view technique, some protocols do not specify a preference between 32-projection and 64-projection methods for conducting myocardial perfusion scintigraphy (MPS), which shows the lack of consensus in this matter. Nevertheless, these guidelines and protocols have not provided us with compelling evidence to support why the 64-projection technique is usually chosen. Thus, we aimed to determine if there is a significant difference between them in the assessment of cardiac perfusion and functional indices.

Methods and results

Sixty-nine patients were included in this pilot prospective, cross-sectional, cross-over, same patient control protocol study and underwent 32- and 64-projection MPS at both stress and rest phases after injecting 740-925 MBq of 99mTc-MIBI for every patient. Then, cardiac indices, including summed stress, rest, and difference scores, extent-stress and rest, left ventricular volumes and ejection-fraction, peak filling rate (PFR), and time to peak filling rate (TTPF) were recorded. Lin's concordance correlation coefficient was used to assess the agreement between protocols, and a paired sample t-test was used to compare the means of variables where appropriate. Findings revealed no significant difference as well as excellent/good agreement between the two methods in either the stress or rest state, except for the TTPF and PFR.

Conclusion

The findings suggest that lower-projection techniques could be adequate for routine clinical assessments without sacrificing diagnostic accuracy. However, the poor agreement for PFR and TTPF indicates that the 32-projection method may not reliably assess diastolic function, implying that the 64-projection protocol is preferable when precise evaluations are necessary.

Towards high-performance deep learning architecture and hardware accelerator design for robust analysis in diffuse correlation spectroscopy

This study proposes a compact deep learning (DL) architecture and a highly parallelized computing hardware platform to reconstruct the blood flow index (BFi) in diffuse correlation spectroscopy (DCS). We leveraged a rigorous analytical model to generate autocorrelation functions (ACFs) to train the DL network. We assessed the accuracy of the proposed DL using simulated and milk phantom data. Compared to convolutional neural networks (CNN), our lightweight DL architecture achieves 66.7% and 18.5% improvement in MSE for BFi and the coherence factor β, using synthetic data evaluation. The accuracy of rBFi over different algorithms was also investigated. We further simplified the DL computing primitives using subtraction for feature extraction, considering further hardware implementation. We extensively explored computing parallelism and fixed-point quantization within the DL architecture. With the DL model's compact size, we employed unrolling and pipelining optimizations for computation-intensive for-loops in the DL model while storing all learned parameters in on-chip BRAMs. We also achieved pixel-wise parallelism, enabling simultaneous, real-time processing of 10 and 15 autocorrelation functions on Zynq-7000 and Zynq-UltraScale+ field programmable gate array (FPGA), respectively. Unlike existing FPGA accelerators that produce BFi and the β from autocorrelation functions on standalone hardware, our approach is an encapsulated, end-to-end on-chip conversion process from intensity photon data to the temporal intensity ACF and subsequently reconstructing BFi and β. This hardware platform achieves an on-chip solution to replace post-processing and miniaturize modern DCS systems that use single-photon cameras. We also comprehensively compared the computational efficiency of our FPGA accelerator to CPU and GPU solutions.

Study of Time-Resolved Dynamics in Turbid Medium Using a Single-Cavity Dual-Comb Laser

In measuring cerebral blood flow (CBF) noninvasively using optical techniques, diffusing-wave spectroscopy is often combined with near-infrared spectroscopy to obtain a reliable blood flow index. Measuring the blood flow index at a determined depth remains the ultimate goal. In this study, we present a simple approach using dual-comb lasers where we simultaneously measure the absorption coefficient (μa), the reduced scattering coefficient (μs '), and dynamic properties. This system can also effectively differentiate dynamics from various depths, which is crucial for analyzing multilayer dynamics. For CBF measurements, this capability is particularly valuable as it helps mitigate the influence of the scalp and skull, thereby enhancing the specificity of deep tissue.

Investigating the effect of limited spectral information on NIRS-derived changes in hemoglobin and cytochrome-c-oxidase concentration with a diffusion-based model

This paper investigates the theoretical capability of near-infrared spectroscopy (NIRS) systems to accurately measure changes in the oxidation state of cerebral cytochrome-c-oxidase (CCO) alongside the hemoglobins, for a deeper understanding of NIRS limitations. Concentration changes of oxy and deoxyhemoglobin (HbO and HbR) indicate the oxygen status of blood vessels and correlate with several other physiological parameters across different pathologies. The oxidation state of CCO indicates cellular energy usage efficiency through oxidative metabolism, potentially serving as a biomarker for brain and other tissue disorders. This study employs an analytical model based on the diffusion equation and statistical analyses to explore the dependency of estimated concentration changes on various systematic parameters, such as choice of wavelengths, spectral bandwidth, and uncertainties in extinction coefficient (ε) and differential pathlength factor (DPF). When there is a 10% uncertainty in DPF and ε, errors were found to be highly dependent on the number of discrete wavelengths, but not on their bandwidth if appropriate considerations are taken to account for it.

A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry to this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, sensors, and correlators), as well as data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed and an outlook for the future directions is provided, to offer effective guidance to embark on DCS research.

A Device-on-Chip Solution for Real-Time Diffuse Correlation Spectroscopy Using FPGA

Diffuse correlation spectroscopy (DCS) is a non-invasive technology for the evaluation of blood perfusion in deep tissue. However, it requires high computational resources for data analysis, which poses challenges in its implementation for real-time applications. To address the unmet need, we developed a novel device-on-chip solution that fully integrates all the necessary computational components needed for DCS. It takes the output of a photon detector and determines the blood flow index (BFI). It is implemented on a field-programmable gate array (FPGA) chip including a multi-tau correlator for the calculation of the temporal light intensity autocorrelation function and a DCS analyzer to perform the curve fitting operation that derives the BFI at a rate of 6000 BFIs/s. The FPGA DCS system was evaluated against a lab-standard DCS system for both phantom and cuff ischemia studies. The results indicate that the autocorrelation of the light correlation and BFI from both the FPGA DCS and the reference DCS matched well. Furthermore, the FPGA DCS system was able to achieve a measurement rate of 50 Hz and resolve pulsatile blood flow. This can significantly lower the cost and footprint of the computational components of DCS and pave the way for portable, real-time DCS systems.

Interferometric near-infrared spectroscopy (iNIRS) reveals that blood flow index depends on wavelength

Blood flow index (BFI) is an optically accessible parameter, with unit distance-squared-over-time, that is widely used as a proxy for tissue perfusion. BFI is defined as the dynamic scattering probability (i.e. the ratio of dynamic to overall reduced scattering coefficients) times an effective Brownian diffusion coefficient that describes red blood cell (RBC) motion. Here, using a wavelength division multiplexed, time-of-flight- (TOF) - resolved iNIRS system, we obtain TOF-resolved field autocorrelations at 773 nm and 855 nm via the same source and collector. We measure the human forearm, comprising biological tissues with mixed static and dynamic scattering, as well as a purely dynamic scattering phantom. Our primary finding is that forearm BFI increases from 773 nm to 855 nm, though the magnitude of this increase varies across subjects (23% ± 19% for N = 3). However, BFI is wavelength-independent in the purely dynamic scattering phantom. From these data, we infer that the wavelength-dependence of BFI arises from the wavelength-dependence of the dynamic scattering probability. This inference is further supported by RBC scattering literature. Our secondary finding is that the higher-order cumulant terms of the mean squared displacement (MSD) of RBCs are significant, but decrease with wavelength. Thus, laser speckle and related modalities should exercise caution when interpreting field autocorrelations.

Measuring pulsatile cortical blood flow and volume during carotid endarterectomy

Carotid endarterectomy (CEA) involves removal of plaque in the carotid artery to reduce the risk of stroke and improve cerebral perfusion. This study aimed to investigate the utility of assessing pulsatile blood volume and flow during CEA. Using a combined near-infrared spectroscopy/diffuse correlation spectroscopy instrument, pulsatile hemodynamics were assessed in 12 patients undergoing CEA. Alterations to pulsatile amplitude, pulse transit time, and beat morphology were observed in measurements ipsilateral to the surgical side. The additional information provided through analysis of pulsatile hemodynamic signals has the potential to enable the discovery of non-invasive biomarkers related to cortical perfusion.

Comprehensive workflow and its validation for simulating diffuse speckle statistics for optical blood flow measurements

Diffuse optical methods including speckle contrast optical spectroscopy and tomography (SCOS and SCOT), use speckle contrast (κ) to measure deep blood flow. In order to design practical systems, parameters such as signal-to-noise ratio (SNR) and the effects of limited sampling of statistical quantities, should be considered. To that end, we have developed a method for simulating speckle contrast signals including effects of detector noise. The method was validated experimentally, and the simulations were used to study the effects of physical and experimental parameters on the accuracy and precision of κ. These results revealed that systematic detector effects resulted in decreased accuracy and precision of κ in the regime of low detected signals. The method can provide guidelines for the design and usage of SCOS and/or SCOT instruments.

DCS blood flow index underestimates skeletal muscle perfusion in vivo: rationale and early evidence for the NIRS-DCS perfusion index

Significance

Diffuse correlation spectroscopy (DCS) permits non-invasive assessment of skeletal muscle blood flow but may misestimate changes in muscle perfusion.

Aim

We aimed to highlight recent evidence that DCS blood flow index (BFI) misestimates changes in muscle blood flow during physiological perturbation and to introduce a novel approach that adjusts BFI for estimated changes in vasodilation.

Approach

We measured changes in muscle BFI during quadriceps and forearm exercises using DCS, the latter of which were adjusted for estimated changes in microvascular flow area and then compared to Doppler ultrasound in the brachial artery. Then, we compared adjusted BFI- and arterial spin labeling (ASL) MRI measures of gastrocnemius blood flow during reactive hyperemia and plantar flexion exercise.

Results

We observed little-to-no change in quadriceps BFI during maximal-effort exercise. Similarly, forearm BFI was modestly increased during handgrip exercise, but the magnitude was significantly lower than measured by Doppler ultrasound in the brachial artery. However, this difference was ameliorated after adjusting BFI for estimated changes in microvascular flow area. Similar observations were also observed in the gastrocnemius when directly comparing the adjusted BFI values to ASL-MRI.

Conclusions

Adjusting BFI for estimated changes in microvascular flow area may improve DCS estimates of muscle blood flow, but further study is needed to validate these methods moving forward.

Validation of the Openwater wearable optical system: cerebral hemodynamic monitoring during a breath-hold maneuver

Significance

Bedside cerebral blood flow (CBF) monitoring has the potential to inform and improve care for acute neurologic diseases, but technical challenges limit the use of existing techniques in clinical practice.

Aim

Here, we validate the Openwater optical system, a novel wearable headset that uses laser speckle contrast to monitor microvascular hemodynamics.

Approach

We monitored 25 healthy adults with the Openwater system and concurrent transcranial Doppler (TCD) while performing a breath-hold maneuver to increase CBF. Relative blood flow (rBF) was derived from changes in speckle contrast, and relative blood volume (rBV) was derived from changes in speckle average intensity.

Results

A strong correlation was observed between beat-to-beat optical rBF and TCD-measured cerebral blood flow velocity (CBFv), R = 0.79 ; the slope of the linear fit indicates good agreement, 0.87 (95% CI: 0.83 - 0.92 ). Beat-to-beat rBV and CBFv were also strongly correlated, R = 0.72 , but as expected the two variables were not proportional; changes in rBV were smaller than CBFv changes, with linear fit slope of 0.18 (95% CI: 0.17 to 0.19). Further, strong agreement was found between rBF and CBFv waveform morphology and related metrics.

Conclusions

This first in vivo validation of the Openwater optical system highlights its potential as a cerebral hemodynamic monitor, but additional validation is needed in disease states.

Comparing the performance potential of speckle contrast optical spectroscopy and diffuse correlation spectroscopy for cerebral blood flow monitoring using Monte Carlo simulations in realistic head geometries

Significance

The non-invasive measurement of cerebral blood flow based on diffuse optical techniques has seen increased interest as a research tool for cerebral perfusion monitoring in critical care and functional brain imaging. Diffuse correlation spectroscopy (DCS) and speckle contrast optical spectroscopy (SCOS) are two such techniques that measure complementary aspects of the fluctuating intensity signal, with DCS quantifying the temporal fluctuations of the signal and SCOS quantifying the spatial blurring of a speckle pattern. With the increasing interest in the use of these techniques, a thorough comparison would inform new adopters of the benefits of each technique.

Aim

We systematically evaluate the performance of DCS and SCOS for the measurement of cerebral blood flow.

Approach

Monte Carlo simulations of dynamic light scattering in an MRI-derived head model were performed. For both DCS and SCOS, estimates of sensitivity to cerebral blood flow changes, coefficient of variation of the measured blood flow, and the contrast-to-noise ratio of the measurement to the cerebral perfusion signal were calculated. By varying complementary aspects of data collection between the two methods, we investigated the performance benefits of different measurement strategies, including altering the number of modes per optical detector, the integration time/fitting time of the speckle measurement, and the laser source delivery strategy.

Results

Through comparison across these metrics with simulated detectors having realistic noise properties, we determine several guiding principles for the optimization of these techniques and report the performance comparison between the two over a range of measurement properties and tissue geometries. We find that SCOS outperforms DCS in terms of contrast-to-noise ratio for the cerebral blood flow signal in the ideal case simulated here but note that SCOS requires careful experimental calibrations to ensure accurate measurements of cerebral blood flow.

Conclusion

We provide design principles by which to evaluate the development of DCS and SCOS systems for their use in the measurement of cerebral blood flow.

Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method

Significance

Diffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient μ a and reduced scattering coefficient μ s ' ) and scalp and skull thicknesses.

Aim

We aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light's temporal ACFs. Moreover, the proposed method can be applied to a range of source-detector distances instead of being limited to a specific source-detector distance.

Approach

We present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural network (DCS-NET), for BFi and coherent factor (β ) estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source-detector distances (5, 10, 15, 20, 25, and 30 mm) on BFi and β estimations among DCS-NET, semi-infinite, and three-layer fitting models.

Results

DCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of μ a and μ s ' at a source-detector separation of 30 mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively.

Conclusions

DCS-NET can robustly quantify blood flow measurements at considerable source-detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous real-time blood flow measurements.

Diffuse Correlation Spectroscopy: A Review of Recent Advances in Parallelisation and Depth Discrimination Techniques

Diffuse correlation spectroscopy is a non-invasive optical modality used to measure cerebral blood flow in real time, and it has important potential applications in clinical monitoring and neuroscience. As such, many research groups have recently been investigating methods to improve the signal-to-noise ratio, imaging depth, and spatial resolution of diffuse correlation spectroscopy. Such methods have included multispeckle, long wavelength, interferometric, depth discrimination, time-of-flight resolution, and acousto-optic detection strategies. In this review, we exhaustively appraise this plethora of recent advances, which can be used to assess limitations and guide innovation for future implementations of diffuse correlation spectroscopy that will harness technological improvements in the years to come.

Comparison of numerical-integration-based methods for blood flow estimation in diffuse correlation spectroscopy

BACKGROUND AND OBJECTIVE

Diffuse correlation spectroscopy (DCS) is an optical blood flow monitoring technology that has been utilized in various biomedical applications. In signal processing of DCS, nonlinear fitting of the experimental data and the theoretical model can be a hindrance in real-time blood flow monitoring. As one of the approaches to resolve the issue, INISg1, the inverse of numerical integration of squared g1 (a normalized electric field autocorrelation function), that could surpass the state-of-the-art technique at the time in terms of signal processing speed, has been introduced. While it is possible to implement INISg1 using various numerical integration methods, no relevant studies have been performed. Meanwhile, INISg1 was only tested within limited experimental conditions, which cannot guarantee the robustness of INISg1 in various experimental conditions. Thus, this study aims to introduce variants of INISg1 and perform a thorough comparison of the original INISg1 and its variants.

METHODS

In this study, based on the right Riemann sum (RR) and trapezoid rule (TR) of numerical integration, INISg1_RR and INISg1_TR are suggested. They are thoroughly compared with the original INISg1 using model-based simulations that offer us control of most of the experimental conditions, including integration time, β, and photon count rate.

RESULTS

Except for some extreme cases, INISg1 performed more robustly than INISg1_RR and INISg1_TR. However, in extreme conditions, variants of INISg1 performed better than INISg1. With the same condition, the signal processing speed of INISg1 was 1.63 and 1.98 times faster than INISg1_RR and INISg1_TR, respectively.

CONCLUSION

This study shows that INISg1 is robust in most cases and the study can be a guide for researchers using INISg1 and its variants in different types of DCS applications.

Novel approaches to measure transfusion effectiveness

PURPOSE OF REVIEW

This review encompasses different considerations of transfusion effectiveness based upon clinical scenario and transfusion indication. Tissue oxygenation, cerebral metabolic oxygen use, and red blood cell (RBC) survival are important elements of transfusion effectiveness in individuals with acute and chronic transfusion requirements.

RECENT FINDINGS

Noninvasive measures of tissue and cerebral oxygen extraction include near-infrared spectroscopy (NIRS) and specialized MRI sequences. RBC survival timepoints including 24 h posttransfusion recovery, 50% recovery timepoint, and mean potential lifespan may be accurately measured with biotin-labeling of RBC prior to transfusion. Labeling at different cell surface densities allows survival of multiple RBC populations to be determined.

SUMMARY

Although past trials of optimal transfusion thresholds have focused on Hb as a singular marker for transfusion needs, measures of oxygenation (via NIRS or specialized MRI) and RBC survival (via biotin labeling) provide the opportunity to personalize transfusion decisions to individual patient's acute health needs or chronic transfusion goals.

Hemoglobin signal network mapping reveals novel indicators for precision medicine

Precision medicine currently relies on a mix of deep phenotyping strategies to guide more individualized healthcare. Despite being widely available and information-rich, physiological time-series measures are often overlooked as a resource to extend insights gained from such measures. Here we have explored resting-state hemoglobin measures applied to intact whole breasts for two subject groups - women with confirmed breast cancer and control subjects - with the goal of achieving a more detailed assessment of the cancer phenotype from a non-invasive measure. Invoked is a novel ordinal partition network method applied to multivariate measures that generates a Markov chain, thereby providing access to quantitative descriptions of short-term dynamics in the form of several classes of adjacency matrices. Exploration of these and their associated co-dependent behaviors unexpectedly reveals features of structured dynamics, some of which are shown to exhibit enzyme-like behaviors and sensitivity to recognized molecular markers of disease. Thus, findings obtained strongly indicate that despite the use of a macroscale sensing method, features more typical of molecular-cellular processes can be identified. Discussed are factors unique to our approach that favor a deeper depiction of tissue phenotypes, its extension to other forms of physiological time-series measures, and its expected utility to advance goals of precision medicine.

Interferometric speckle visibility spectroscopy (iSVS) for measuring decorrelation time and dynamics of moving samples with enhanced signal-to-noise ratio and relaxed reference requirements

Diffusing wave spectroscopy (DWS) is a group of techniques used to measure the dynamics of a scattering medium in a non-invasive manner. DWS methods rely on detecting the speckle light field from the moving scattering medium and measuring the speckle decorrelation time to quantify the scattering medium's dynamics. For DWS, the signal-to-noise (SNR) is determined by the ratio between measured decorrelation time to the standard error of the measurement. This SNR is often low in certain applications because of high noise variances and low signal intensity, especially in biological applications with restricted exposure and emission levels. To address this photon-limited signal-to-noise ratio problem, we investigated, theoretically and experimentally, the SNR of an interferometric speckle visibility spectroscopy (iSVS) compared to more traditional DWS methods. We found that iSVS can provide excellent SNR performance through its ability to overcome camera noise. We also proved an iSVS system has more relaxed constraints on the reference beam properties. For an iSVS system to function properly, we only require the reference beam to exhibit local temporal stability, while incident angle, reference phase and intensity uniformity do not need to be constrained. This flexibility can potentially enable more unconventional iSVS implementation schemes.

Assessing depth sensitivity in laser interferometry speckle visibility spectroscopy (iSVS) through source-to-detector distance variation and cerebral blood flow monitoring in humans and rabbits

Recently, speckle visibility spectroscopy (SVS) was non-invasively applied on the head to monitor cerebral blood flow. The technique, using a multi-pixel detecting device (e.g., camera), allows the detection of a larger number of speckles, increasing the proportion of light that is detected. Due to this increase, it is possible to collect light that has propagated deeper through the brain. As a direct consequence, cerebral blood flow can be monitored. However, isolating the cerebral blood flow from the other layers, such as the scalp or skull components, remains challenging. In this paper, we report our investigations on the depth-sensitivity of laser interferometry speckle visibility spectroscopy (iSVS). Specifically, we varied the depth of penetration of the laser light into the head by tuning the source-to-detector distance, and identified the transition point at which cerebral blood flow in humans and rabbits starts to be detected.

Measuring human cerebral blood flow and brain function with fiber-based speckle contrast optical spectroscopy system

Cerebral blood flow (CBF) is crucial for brain health. Speckle contrast optical spectroscopy (SCOS) is a technique that has been recently developed to measure CBF, but the use of SCOS to measure human brain function at large source-detector separations with comparable or greater sensitivity to cerebral rather than extracerebral blood flow has not been demonstrated. We describe a fiber-based SCOS system capable of measuring human brain activation induced CBF changes at 33 mm source detector separations using CMOS detectors. The system implements a pulsing strategy to improve the photon flux and uses a data processing pipeline to improve measurement accuracy. We show that SCOS outperforms the current leading optical modality for measuring CBF, i.e. diffuse correlation spectroscopy (DCS), achieving more than 10x SNR improvement at a similar financial cost. Fiber-based SCOS provides an alternative approach to functional neuroimaging for cognitive neuroscience and health science applications.

Enhancing diffuse correlation spectroscopy pulsatile cerebral blood flow signal with near-infrared spectroscopy photoplethysmography

Significance

Combining near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) allows for quantifying cerebral blood volume, flow, and oxygenation changes continuously and non-invasively. As recently shown, the DCS pulsatile cerebral blood flow index (pCBF i ) can be used to quantify critical closing pressure (CrCP) and cerebrovascular resistance (CVR i ).

Aim

Although current DCS technology allows for reliable monitoring of the slow hemodynamic changes, resolving pulsatile blood flow at large source-detector separations, which is needed to ensure cerebral sensitivity, is challenging because of its low signal-to-noise ratio (SNR). Cardiac-gated averaging of several arterial pulse cycles is required to obtain a meaningful waveform.

Approach

Taking advantage of the high SNR of NIRS, we demonstrate a method that uses the NIRS photoplethysmography (NIRS-PPG) pulsatile signal to model DCS pCBF i , reducing the coefficient of variation of the recovered pulsatile waveform (pCBF i - fit ) and allowing for an unprecedented temporal resolution (266 Hz) at a large source-detector separation (> 3    cm ).

Results

In 10 healthy subjects, we verified the quality of the NIRS-PPG pCBF i - fit during common tasks, showing high fidelity against pCBF i (R 2 0.98 ± 0.01 ). We recovered CrCP and CVR i at 0.25 Hz, > 10 times faster than previously achieved with DCS.

Conclusions

NIRS-PPG improves DCS pCBF i SNR, reducing the number of gate-averaged heartbeats required to recover CrCP and CVR i .

Non-invasive diffuse optical monitoring of cerebral physiology in an adult swine-model of impact traumatic brain injury

In this study, we used diffuse optics to address the need for non-invasive, continuous monitoring of cerebral physiology following traumatic brain injury (TBI). We combined frequency-domain and broadband diffuse optical spectroscopy with diffuse correlation spectroscopy to monitor cerebral oxygen metabolism, cerebral blood volume, and cerebral water content in an established adult swine-model of impact TBI. Cerebral physiology was monitored before and after TBI (up to 14 days post injury). Overall, our results suggest that non-invasive optical monitoring can assess cerebral physiologic impairments post-TBI, including an initial reduction in oxygen metabolism, development of cerebral hemorrhage/hematoma, and brain swelling.

Portable, high speed blood flow measurements enabled by long wavelength, interferometric diffuse correlation spectroscopy (LW-iDCS)

Diffuse correlation spectroscopy (DCS) is an optical technique that can be used to characterize blood flow in tissue. The measurement of cerebral hemodynamics has arisen as a promising use case for DCS, though traditional implementations of DCS exhibit suboptimal signal-to-noise ratio (SNR) and cerebral sensitivity to make robust measurements of cerebral blood flow in adults. In this work, we present long wavelength, interferometric DCS (LW-iDCS), which combines the use of a longer illumination wavelength (1064 nm), multi-speckle, and interferometric detection, to improve both cerebral sensitivity and SNR. Through direct comparison with long wavelength DCS based on superconducting nanowire single photon detectors, we demonstrate an approximate 5× improvement in SNR over a single channel of LW-DCS in the measured blood flow signals in human subjects. We show equivalence of extracted blood flow between LW-DCS and LW-iDCS, and demonstrate the feasibility of LW-iDCS measured at 100 Hz at a source-detector separation of 3.5 cm. This improvement in performance has the potential to enable robust measurement of cerebral hemodynamics and unlock novel use cases for diffuse correlation spectroscopy.

Agreement in cerebrovascular reactivity assessed with diffuse correlation spectroscopy across experimental paradigms improves with short separation regression

Significance

Cerebrovascular reactivity (CVR), i.e., the ability of cerebral vasculature to dilate or constrict in response to vasoactive stimuli, is a biomarker of vascular health. Exogenous administration of inhaled carbon dioxide, i.e., hypercapnia (HC), remains the "gold-standard" intervention to assess CVR. More tolerable paradigms that enable CVR quantification when HC is difficult/contraindicated have been proposed. However, because these paradigms feature mechanistic differences in action, an assessment of agreement of these more tolerable paradigms to HC is needed.

Aim

We aim to determine the agreement of CVR assessed during HC, breath-hold (BH), and resting state (RS) paradigms.

Approach

Healthy adults were subject to HC, BH, and RS paradigms. End tidal carbon dioxide (EtCO2) and cerebral blood flow (CBF, assessed with diffuse correlation spectroscopy) were monitored continuously. CVR (%/mmHg) was quantified via linear regression of CBF versus EtCO2 or via a general linear model (GLM) that was used to minimize the influence of systemic and extracerebral signal contributions.

Results

Strong agreement ( CCC ≥ 0.69 ; R ≥ 0.76 ) among CVR paradigms was demonstrated when utilizing a GLM to regress out systemic/extracerebral signal contributions. Linear regression alone showed poor agreement across paradigms ( CCC ≤ 0.35 ; R ≤ 0.45 ).

Conclusions

More tolerable experimental paradigms coupled with regression of systemic/extracerebral signal contributions may offer a viable alternative to HC for assessing CVR.

Single-photon detection using high-temperature superconductors

The detection of individual quanta of light is important for quantum communication, fluorescence lifetime imaging, remote sensing and more. Due to their high detection efficiency, exceptional signal-to-noise ratio and fast recovery times, superconducting-nanowire single-photon detectors (SNSPDs) have become a critical component in these applications. However, the operation of conventional SNSPDs requires costly cryocoolers. Here we report the fabrication of two types of high-temperature superconducting nanowires. We observe linear scaling of the photon count rate on the radiation power at the telecommunications wavelength of 1.5 μm and thereby reveal single-photon operation. SNSPDs made from thin flakes of Bi₂Sr₂CaCu₂O_8+δ exhibit a single-photon response up to 25 K, and for SNSPDs from La_1.55Sr_0.45CuO₄/La₂CuO₄ bilayer films, this response is observed up to 8 K. While the underlying detection mechanism is not fully understood yet, our work expands the family of materials for SNSPD technology beyond the liquid helium temperature limit and suggests that even higher operation temperatures may be reached using other high-temperature superconductors.

Epinephrine iontophoresis attenuates changes in skin blood flow and abolishes cutaneous contamination of near-infrared diffuse correlation spectroscopy estimations of muscle perfusion

Near-infrared diffuse correlation spectroscopy (NIR-DCS) is an optical imaging technique for measuring relative changes in skeletal muscle microvascular perfusion (i.e., fold change above baseline) during reactive hyperemia testing and exercise and is reported as a blood flow index (BFI). Although it is generally accepted that changes in BFI are primarily driven by changes in muscle perfusion, it is well known that large, hyperthermia-induced changes in cutaneous blood flow can uncouple this relationship. What remains unknown, is how much of an impact that changes in cutaneous perfusion have on NIR-DCS BFI and estimates of skeletal muscle perfusion under thermoneutral conditions, where changes in cutaneous blood flow are assumed to be relatively low. We therefore used epinephrine iontophoresis to pharmacologically block changes in cutaneous perfusion throughout a battery of experimental procedures. The data show that 1) epinephrine iontophoresis attenuates changes in cutaneous perfusion for up to 4-h posttreatment, even in the face of significant neural and local stimuli, 2) under thermoneutral conditions, cutaneous perfusion does not significantly impact NIR-DCS BFI during reactive hyperemia testing or moderate-intensity exercise, and 3) during passive whole body heat stress, when cutaneous vasodilation is pronounced, epinephrine iontophoresis preserves NIR-DCS measures of skeletal muscle BFI during moderate-intensity exercise. Collectively, these data suggest that cutaneous perfusion is unlikely to have a major impact on NIR-DCS estimates of skeletal muscle BFI under thermoneutral conditions, but that epinephrine iontophoresis can be used to abolish cutaneous contamination of the NIR-DCS BFI signal during studies where skin blood flow may be elevated but skeletal muscle perfusion is of specific interest.

Massively parallel, real-time multispeckle diffuse correlation spectroscopy using a 500 × 500 SPAD camera

Diffuse correlation spectroscopy (DCS) is a promising noninvasive technique for monitoring cerebral blood flow and measuring cortex functional activation tasks. Taking multiple parallel measurements has been shown to increase sensitivity, but is not easily scalable with discrete optical detectors. Here we show that with a large 500 × 500 SPAD array and an advanced FPGA design, we achieve an SNR gain of almost 500 over single-pixel mDCS performance. The system can also be reconfigured to sacrifice SNR to decrease correlation bin width, with 400 ns resolution being demonstrated over 8000 pixels.

Diffuse correlation spectroscopy: current status and future outlook

Diffuse correlation spectroscopy (DCS) has emerged as a versatile, noninvasive method for deep tissue perfusion assessment using near-infrared light. A broad class of applications is being pursued in neuromonitoring and beyond. However, technical limitations of the technology as originally implemented remain as barriers to wider adoption. A wide variety of approaches to improve measurement performance and reduce cost are being explored; these include interferometric methods, camera-based multispeckle detection, and long path photon selection for improved depth sensitivity. We review here the current status of DCS technology and summarize future development directions and the challenges that remain on the path to widespread adoption.

Interferometric diffuse optics: recent advances and future outlook

The field of diffuse optics has provided a rich set of neurophotonic tools to measure the human brain noninvasively. Interferometric detection is a recent, exciting methodological development in this field. The approach is especially promising for the measurement of diffuse fluctuation signals related to blood flow. Benefitting from inexpensive sensor arrays, the interferometric approach has already dramatically improved throughput, enabling the measurement of brain blood flow faster and deeper. The interferometric approach can also achieve time-of-flight resolution, improving the accuracy of acquired signals. We provide a historical perspective and summary of recent work in the nascent area of interferometric diffuse optics. We predict that the convergence of interferometric technology with existing economies of scale will propel many advances in the years to come.

Influence of oversimplifying the head anatomy on cerebral blood flow measurements with diffuse correlation spectroscopy

Significance

Diffuse correlation spectroscopy (DCS) is an emerging optical modality for non-invasive assessment of an index of regional cerebral blood flow. By the nature of this noninvasive measurement, light must pass through extracerebral layers (i.e., skull, scalp, and cerebral spinal fluid) before detection at the tissue surface. To minimize the contribution of these extracerebral layers to the measured signal, an analytical model has been developed that treats the head as a series of three parallel and infinitely extending slabs (mimicking scalp, skull, and brain). The three-layer model has been shown to provide a significant improvement in cerebral blood flow estimation over the typically used model that treats the head as a bulk homogenous medium. However, the three-layer model is still a gross oversimplification of the head geometry that ignores head curvature, the presence of cerebrospinal fluid (CSF), and heterogeneity in layer thickness.

Aim

Determine the influence of oversimplifying the head geometry on cerebral blood flow estimated with the three-layer model.

Approach

Data were simulated with Monte Carlo in a four-layer slab medium and a three-layer sphere medium to isolate the influence of CSF and curvature, respectively. Additionally, simulations were performed on magnetic resonance imaging (MRI) head templates spanning a wide-range of ages. Simulated data were fit to both the homogenous and three-layer model for CBF. Finally, to mitigate the errors in potential CBF estimation due to the difficulty in defining layer thickness, we investigated an approach to identify an equivalent, "optimized" thickness via a pressure modulation.

Results

Both head curvature and failing to account for CSF lead to significant errors in the estimation of CBF. However, the effect of curvature and CSF on relative changes in CBF is minimal. Further, we found that CBF was underestimated in all MRI-templates, although the magnitude of these underestimations was highly influenced by small variations in the source and detector optode positioning. The optimized thickness obtained from pressure modulation did not improve estimation accuracy of CBF, although it did significantly improve the estimation accuracy of relative changes in CBF.

Conclusions

In sum, these findings suggest that the three-layer model holds promise for improving estimation of relative changes in cerebral blood flow; however, estimations of absolute cerebral blood flow with the approach should be viewed with caution given that it is difficult to account for appreciable sources of error, such as curvature and CSF.

Continuous-wave parallel interferometric near-infrared spectroscopy (CW NIRS) with a fast two-dimensional camera

Interferometric near-infrared spectroscopy (iNIRS) is an optical method that noninvasively measures the optical and dynamic properties of the human brain in vivo. However, the original iNIRS technique uses single-mode fibers for light collection, which reduces the detected light throughput. The reduced light throughput is compensated by the relatively long measurement or integration times (∼1 sec), which preclude monitoring of rapid blood flow changes that could be linked to neural activation. Here, we propose parallel interferometric near-infrared spectroscopy (πNIRS) to overcome this limitation. In πNIRS we use multi-mode fibers for light collection and a high-speed, two-dimensional camera for light detection. Each camera pixel acts effectively as a single iNIRS channel. So, the processed signals from each pixel are spatially averaged to reduce the overall integration time. Moreover, interferometric detection provides us with the unique capability of accessing complex information (amplitude and phase) about the light remitted from the sample, which with more than 8000 parallel channels, enabled us to sense the cerebral blood flow with only a 10 msec integration time (∼100x faster than conventional iNIRS). In this report, we have described the theoretical foundations and possible ways to implement πNIRS. Then, we developed a prototype continuous wave (CW) πNIRS system and validated it in liquid phantoms. We used our CW πNIRS to monitor the pulsatile blood flow in a human forearm in vivo. Finally, we demonstrated that CW πNIRS could monitor activation of the prefrontal cortex by recording the change in blood flow in the forehead of the subject while he was reading an unknown text.

Intraoperative Cerebral Hemodynamic Monitoring during Carotid Endarterectomy via Diffuse Correlation Spectroscopy and Near-Infrared Spectroscopy

Objective: This pilot study aims to show the feasibility of noninvasive and real-time cerebral hemodynamic monitoring during carotid endarterectomy (CEA) via diffuse correlation spectroscopy (DCS) and near-infrared spectroscopy (NIRS). Methods: Cerebral blood flow index (CBFi) was measured unilaterally in seven patients and bilaterally in seventeen patients via DCS. In fourteen patients, hemoglobin oxygenation changes were measured bilaterally and simultaneously via NIRS. Cerebral autoregulation (CAR) and cerebrovascular resistance (CVR) were estimated using CBFi and arterial blood pressure data. Further, compensatory responses to the ipsilateral hemisphere were investigated at different contralateral stenosis levels. Results: Clamping of carotid arteries caused a sharp increase of CVR (~70%) and a marked decrease of ipsilateral CBFi (57%). From the initial drop, we observed partial recovery in CBFi, an increase of blood volume, and a reduction in CVR in the ipsilateral hemisphere. There were no significant changes in compensatory responses between different contralateral stenosis levels as CAR was intact in both hemispheres throughout the CEA phase. A comparison between hemispheric CBFi showed lower ipsilateral levels during the CEA and post-CEA phases (p < 0.001, 0.03). Conclusion: DCS alone or combined with NIRS is a useful monitoring technique for real-time assessment of cerebral hemodynamic changes and allows individualized strategies to improve cerebral perfusion during CEA by identifying different hemodynamic metrics.

Functional Time Domain Diffuse Correlation Spectroscopy

Time-domain diffuse correlation spectroscopy (TD-DCS) offers a novel approach to high-spatial resolution functional brain imaging based on the direct quantification of cerebral blood flow (CBF) changes in response to neural activity. However, the signal-to-noise ratio (SNR) offered by previous TD-DCS instruments remains a challenge to achieving the high temporal resolution needed to resolve perfusion changes during functional measurements. Here we present a next-generation optimized functional TD-DCS system that combines a custom 1,064 nm pulse-shaped, quasi transform-limited, amplified laser source with a high-resolution time-tagging system and superconducting nanowire single-photon detectors (SNSPDs). System characterization and optimization was conducted on homogenous and two-layer intralipid phantoms before performing functional CBF measurements in six human subjects. By acquiring CBF signals at over 5 Hz for a late gate start time of the temporal point spread function (TPSF) at 15 mm source-detector separation, we demonstrate for the first time the measurement of blood flow responses to breath-holding and functional tasks using TD-DCS.

Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

Influence of source-detector separation on diffuse correlation spectroscopy measurements of cerebral blood flow with a multilayered analytical model

Significance: Diffuse correlation spectroscopy (DCS) is an emerging noninvasive optical technology for bedside monitoring of cerebral blood flow. However, extracerebral hemodynamics can significantly influence DCS estimations of cerebral perfusion. Advanced analytical models can be used to remove the contribution of extracerebral hemodynamics; however, these models are highly sensitive to measurement noise. There is a need for an empirical determination of the optimal source-detector separation(s) (SDS) that improves the accuracy and reduces sensitivity to noise in the estimation of cerebral blood flow with these models. Aim: To determine the influence of SDS on solution uniqueness, measurement accuracy, and sensitivity to inaccuracies in model parameters when using the three-layer model to estimate cerebral blood flow with DCS. Approach: We performed a series of in silico simulations on samples spanning a wide range of physiologically-relevant layer optical properties, thicknesses, and flow. Data were simulated at SDS ranging from 0.5 to 3.0 cm using the three-layer solution to the correlation diffusion equation (with and without noise added) and using three-layer slab Monte Carlo simulations. We quantified the influence of SDS on uniqueness, accuracy, and sensitivity to inaccuracies in model parameters using the three-layer inverse model. Results: Two SDS are required to ensure a unique solution of cerebral blood flow index (CBFi). Combinations of 0.5/1.0/1.5 and 2.5 cm provide the optimal choice for balancing the depth penetration with signal-to-noise ratio to minimize the error in CBFi across a wide range of samples with varying optical properties, thicknesses, and dynamics. Conclusions: These results suggest that the choice of SDS is critical for minimizing the estimated error of cerebral blood flow when using the three-layer model to analyze DCS data.

Performance optimisation of a holographic Fourier domain diffuse correlation spectroscopy instrument

We have previously demonstrated a novel interferometric multispeckle Fourier domain diffuse correlation spectroscopy system that makes use of holographic camera-based detection, and which is capable of making in vivo pulsatile flow measurements. In this work, we report on a systematic characterisation of the signal-to-noise ratio performance of our system. This includes demonstration and elimination of laser mode hopping, and correction for the instrument's modulation transfer function to ensure faithful reconstruction of measured intensity profiles. We also demonstrate a singular value decomposition approach to ensure that spatiotemporally correlated experimental noise sources do not limit optimal signal-to-noise ratio performance. Finally, we present a novel multispeckle denoising algorithm that allows our instrument to achieve a signal-to-noise ratio gain that is equal to the square root of the number of detected speckles, whilst detecting up to ∼1290 speckles in parallel. The signal-to-noise ratio gain of 36 that we report is a significant step toward mitigating the trade-off that exists between signal-to-noise ratio and imaging depth in diffuse correlation spectroscopy.

Diffuse Correlation Spectroscopy Beyond the Water Peak Enabled by Cross-Correlation of the Signals From InGaAs/InP Single Photon Detectors

OBJECTIVE

Diffuse correlation spectroscopy (DCS) is an optical technique that allows for the non-invasive measurement of blood flow. Recent work has shown that utilizing longer wavelengths beyond the traditional NIR range provides a significant improvement to signal-to-noise ratio (SNR). However, current detectors both sensitive to longer wavelengths and suitable for clinical applications (InGaAs/InP SPADs) suffer from suboptimal afterpulsing and dark noise characteristics. To overcome these barriers, we introduce a cross correlation method to more accurately recover blood flow information using InGaAs/InP SPADs.

METHODS

Two InGaAs/InP SPAD detectors were used for during in vitro and in vivo DCS measurements. Cross correlation of the photon streams from each detector was performed to calculate the correlation function. Detector operating parameters were varied to determine parameters which maximized measurement SNR.State-space modeling was performed to determine the detector characteristics at each operating point.

RESULTS

Evaluation of detector characteristics was performed across the range of operating conditions. Modeling the effects of the detector noise on the correlation function provided a method to correct the distortion of the correlation curve, yielding accurate recovery of flow information as confirmed by a reference detector.

CONCLUSION

Through a combination of cross-correlation of the signals from two detectors, model-based characterization of detector response, and optimization of detector operating parameters, the method allows for the accurate estimation of the true blood flow index.

SIGNIFICANCE

This work presents a method by which DCS can be performed at longer NIR wavelengths with existing detector technology, taking advantage of the increased SNR.

Complete head cerebral sensitivity mapping for diffuse correlation spectroscopy using subject-specific magnetic resonance imaging models

We characterize cerebral sensitivity across the entire adult human head for diffuse correlation spectroscopy, an optical technique increasingly used for bedside cerebral perfusion monitoring. Sixteen subject-specific magnetic resonance imaging-derived head models were used to identify high sensitivity regions by running Monte Carlo light propagation simulations at over eight hundred uniformly distributed locations on the head. Significant spatial variations in cerebral sensitivity, consistent across subjects, were found. We also identified correlates of such differences suitable for real-time assessment. These variations can be largely attributed to changes in extracerebral thickness and should be taken into account to optimize probe placement in experimental settings.

Fast diffuse correlation spectroscopy with a low-cost, fiber-less embedded diode laser

Diffuse correlation spectroscopy (DCS), a popular optical technique for fast noninvasive measurement of blood flow, is commonly implemented using expensive fiber-coupled long coherence length laser systems. Here, we report the development of a portable and fiber-less approach that can be used as a low-cost alternative to illuminate tissue in DCS instruments. We validate the accuracy and noise characteristics of the fiber-less DCS laser source, by comparisons against traditional DCS light sources, with experiments on controlled tissue-simulating phantoms and in humans.

Performance assessment of laser sources for time-domain diffuse correlation spectroscopy

Time-domain diffuse correlation spectroscopy (TD-DCS) is an emerging optical technique that enables noninvasive measurement of microvascular blood flow with photon path-length resolution. In TD-DCS, a picosecond pulsed laser with a long coherence length, adequate illumination power, and narrow instrument response function (IRF) is required, and satisfying all these features is challenging. To this purpose, in this study we characterized the performance of three different laser sources for TD-DCS. First, the sources were evaluated based on their emission spectrum and IRF. Then, we compared the signal-to-noise ratio and the sensitivity to velocity changes of scattering particles in a series of phantom measurements. We also compared the results for in vivo measurements, performing an arterial occlusion protocol on the forearm of three adult subjects. Overall, each laser has the potential to be successfully used both for laboratory and clinical applications. However, we found that the effects caused by the IRF are more significant than the effect of a limited temporal coherence.

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. We utilized the diffuse correlation spectroscopy method for assessing cerebral blood flow at the neuro-ICU in a patient with traumatic brain injury. The present case demonstrates the feasibility of continuous recording of noninvasive cerebral blood flow transients that correlated well with the gold-standard invasive measurements and with the frequency content changes in the EEG data.

Impact of Cutaneous Blood Flow on NIR-DCS Measures of Skeletal Muscle Blood Flow Index

Near-infrared diffuse correlation spectroscopy (NIR-DCS) is an optical technique for estimating relative changes in skeletal muscle perfusion during exercise, but may be affected by changes in cutaneous blood flow, as photons emitted by the laser must first pass through the skin. Accordingly, the purpose of this investigation was to examine how increased cutaneous blood flow affects NIR-DCS blood flow index (BFI) at rest and during exercise using a passive whole-body heating protocol that increases cutaneous, but not skeletal muscle, perfusion in the uncovered limb. BFI and cutaneous perfusion (laser Doppler flowmetry) were assessed in 15 healthy young subjects before (e.g., rest) and during 5-minutes of moderate-intensity hand-grip exercise in normothermic conditions and after cutaneous blood flow was elevated via whole-body heating. Hyperthermia significantly increased both cutaneous perfusion (~7.3-fold; p≤0.001) and NIR-DCS BFI (~4.5-fold; p≤0.001). Although relative BFI (i.e., fold-change above baseline) exhibited a typical exponential increase in muscle perfusion during normothermic exercise (2.81±0.95), there was almost no change in BFI during hyperthermic exercise (1.43±0.44). A subset of 8 subjects were subsequently treated with intradermal injection of botulinum toxin-A (Botox) to block heating-induced elevations in cutaneous blood flow, which 1) nearly abolished the hyperthermia-induced increase in BFI, and 2) restored BFI kinetics during hyperthermic exercise to values that were not different from normothermic exercise (p=0.091). Collectively, our results demonstrate that cutaneous blood flow can have a substantial, detrimental impact on NIR-DCS estimates of skeletal muscle perfusion and highlight the need for technical and/or pharmacological advancements to overcome this issue moving forward.

Superconducting nanowire single-photon sensing of cerebral blood flow

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.

Optimization of time domain diffuse correlation spectroscopy parameters for measuring brain blood flow

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.

Recipes for diffuse correlation spectroscopy instrument design using commonly utilized hardware based on targets for signal-to-noise ratio and precision

Over the recent years, a typical implementation of diffuse correlation spectroscopy (DCS) instrumentation has been adapted widely. However, there are no detailed and accepted recipes for designing such instrumentation to meet pre-defined signal-to-noise ratio (SNR) and precision targets. These require specific attention due to the subtleties of the DCS signals. Here, DCS experiments have been performed using liquid tissue simulating phantoms to study the effect of the detected photon count-rate, the number of parallel detection channels and the measurement duration on the precision and SNR to suggest scaling relations to be utilized for device design.

The LUCA device: a multi-modal platform combining diffuse optics and ultrasound imaging for thyroid cancer screening

We present the LUCA device, a multi-modal platform combining eight-wavelength near infrared time resolved spectroscopy, sixteen-channel diffuse correlation spectroscopy and a clinical ultrasound in a single device. By simultaneously measuring the tissue hemodynamics and performing ultrasound imaging, this platform aims to tackle the low specificity and sensitivity of the current thyroid cancer diagnosis techniques, improving the screening of thyroid nodules. Here, we show a detailed description of the device, components and modules. Furthermore, we show the device tests performed through well established protocols for phantom validation, and the performance assessment for in vivo. The characterization tests demonstrate that LUCA device is capable of performing high quality measurements, with a precision in determining in vivo tissue optical and dynamic properties of better than 3%, and a reproducibility of better than 10% after ultrasound-guided probe repositioning, even with low photon count-rates, making it suitable for a wide variety of clinical applications.