Establishing the diffuse correlation spectroscopy signal relationship with blood flow

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.

Optical, contact-free assessment of brain tissue stiffness and neurodegeneration

Dementia affects a large proportion of the world's population. Approaches that allow for early disease detection and non-invasive monitoring of disease progression are desperately needed. Current approaches are centred on costly imaging technologies such as positron emission tomography and magnetic resonance imaging. We propose an alternative approach to assess neurodegeneration based on diffuse correlation spectroscopy (DCS), a remote and optical sensing technique. We employ this approach to assess neurodegeneration in mouse brains from healthy animals and those with prion disease. We find a statistically significant difference in the optical speckle decorrelation times between prion-diseased and healthy animals. We directly calibrated our DCS technique using hydrogel samples of varying Young's modulus, indicating that we can optically measure changes in the brain tissue stiffness in the order of 60 Pa (corresponding to a 1 s change in speckle decorrelation time). DCS holds promise for contact-free assessment of tissue stiffness alteration due to neurodegeneration, with a similar sensitivity to contact-based (e.g. nanoindentation) approaches.

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.

Proof of concept validation of bioresorbable optical fibers for diffuse correlation spectroscopy

Optical quality bioresorbable materials have been gaining interest in recent years for various interstitial biomedical/medical application. An example of this is when the implant gradually dissolves in the body, providing physiological information over extended periods of time, hence reducing the need for revision surgeries. This study reports for the first time the in-house fabrication of single mode (at 785 nm) calcium phosphate glass (CPG) based bioresorbable optical fibers and investigates their suitability for microvascular blood flow monitoring using diffuse correlation spectroscopy (DCS). Ex vivo experiments in liquid phantom and non-invasive in vivo experiments on the human forearm muscle were conducted using multimode and single mode CPG bioresorbable optical fibers. The retrieved flow index from the correlation curves acquired using CPG fibers was in good agreement with that obtained using standard silica (Si) fibers, both ex vivo and in vivo. The results demonstrate the potential of CPG optical fibers for further exploration.

ATLAS: a large array, on-chip compute SPAD camera for multispeckle diffuse correlation spectroscopy

We present ATLAS, a 512 × 512 single-photon avalanche diode (SPAD) array with embedded autocorrelation computation, implemented in 3D-stacked CMOS technology, suitable for single-photon correlation spectroscopy applications, including diffuse correlation spectroscopy (DCS). The shared per-macropixel SRAM architecture provides a 128 × 128 macropixel resolution, with parallel autocorrelation computation, with a minimum autocorrelation lag-time of 1 µs. We demonstrate the direct, on-chip computation of the autocorrelation function of the sensor, and its capability to resolve changes in decorrelation times typical of body tissue in real time, at long source-detector separations similar to those achieved by the current leading optical modalities for cerebral blood flow monitoring. Finally, we demonstrate the suitability for in-vivo measurements through cuff-occlusion and forehead cardiac signal measurements.

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.

How the heterogeneity of the severely injured brain affects hybrid diffuse optical signals: case examples and guidelines

Significance

A shortcoming of the routine clinical use of diffuse optics (DO) in the injured head has been that the results from commercial near-infrared spectroscopy-based devices are not reproducible, often give physiologically invalid values, and differ among systems. Besides the limitations due to the physics of continuous-wave light sources, one culprit is the head heterogeneity and the underlying morphological and functional abnormalities of the probed tissue.

Aim

The aim is to investigate the effect that different tissue alterations in the damaged head have on DO signals and provide guidelines to avoid data misinterpretation.

Approach

DO measurements and computed tomography scans were acquired on brain-injured patients. The relationship between the signals and the underlying tissue types was classified on a case-by-case basis.

Results

Examples and suggestions to establish quality control routines were provided. The findings suggested guidelines for carrying out DO measurements and speculations toward improved devices.

Conclusions

We advocate for the standardization of the DO measurements to secure a role for DO in neurocritical care. We suggest that blind measurements are unacceptably problematic due to confounding effects and care using a priori and a posteriori quality control routines that go beyond an assessment of the signal-to-noise ratio that is typically utilized.

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.

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

This study 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-shape 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-shape 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% CO2 inhalation and transient carotid artery ligations.

Conclusions

Compared to conventional point scanning, the 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.

Compact and cost-effective laser-powered speckle contrast optical spectroscopy fiber-free device for measuring cerebral blood flow

Significance

In the realm of cerebrovascular monitoring, primary metrics typically include blood pressure, which influences cerebral blood flow (CBF) and is contingent upon vessel radius. Measuring CBF noninvasively poses a persistent challenge, primarily attributed to the difficulty of accessing and obtaining signal from the brain.

Aim

Our study aims to introduce a compact speckle contrast optical spectroscopy device for noninvasive CBF measurements at long source-to-detector distances, offering cost-effectiveness, and scalability while tracking blood flow (BF) with remarkable sensitivity and temporal resolution.

Approach

The wearable sensor module consists solely of a laser diode and a board camera. It can be easily placed on a subject's head to measure BF at a sampling rate of 80 Hz.

Results

Compared to the single-fiber-based version, the proposed device achieved a signal gain of about 70 times, showed superior stability, reproducibility, and signal-to-noise ratio for measuring BF at long source-to-detector distances. The device can be distributed in multiple configurations around the head.

Conclusions

Given its cost-effectiveness, scalability, and simplicity, this laser-centric tool offers significant potential in advancing noninvasive cerebral monitoring technologies.

Multi-wavelength multi-distance diffuse correlation spectroscopy system for assessment of premature infants' cerebral hemodynamics

Infants born at an extremely low gestational age (ELGA, < 29 weeks) are at an increased risk of intraventricular hemorrhage (IVH), and there is a need for standalone, safe, easy-to-use tools for monitoring cerebral hemodynamics. We have built a multi-wavelength multi-distance diffuse correlation spectroscopy device (MW-MD-DCS), which utilizes time-multiplexed, long-coherence lasers at 785, 808, and 853 nm, to simultaneously quantify the index of cerebral blood flow (CBFi) and the hemoglobin oxygen saturation (SO2). We show characterization data on liquid phantoms and demonstrate the system performance on the forearm of healthy adults, as well as clinical data obtained on two preterm infants.

A compact and cost-effective laser-powered speckle visibility spectroscopy (SVS) device for measuring cerebral blood flow

In the realm of cerebrovascular monitoring, primary metrics typically include blood pressure, which influences cerebral blood flow (CBF) and is contingent upon vessel radius. Measuring CBF non-invasively poses a persistent challenge, primarily attributed to the difficulty of accessing and obtaining signal from the brain. This study aims to introduce a compact speckle visibility spectroscopy (SVS) device designed for non-invasive CBF measurements, offering cost-effectiveness and scalability while tracking CBF with remarkable sensitivity and temporal resolution. The wearable hardware has a modular design approach consisting solely of a laser diode as the source and a meticulously selected board camera as the detector. They both can be easily placed on a subject's head to measure CBF with no additional optical elements. The SVS device can achieve a sampling rate of 80 Hz with minimal susceptibility to external disturbances. The device also achieves better SNR compared with traditional fiber-based SVS devices, capturing about 70 times more signal and showing superior stability and reproducibility. It is designed to be paired and distributed in multiple configurations around the head, and measure signals that exceed the quality of prior optical CBF measurement techniques. Given its cost-effectiveness, scalability, and simplicity, this laser-centric tool offers significant potential in advancing non-invasive cerebral monitoring technologies.

Wide-field intensity fluctuation imaging

The temporal intensity fluctuations contain important information about the light source and light-medium interaction and are typically characterized by the intensity autocorrelation function, g2(τ). The measurement of g2(τ) is a central topic in many optical sensing applications, ranging from stellar intensity interferometer in astrophysics, to fluorescence correlation spectroscopy in biomedical sciences and blood flow measurement with dynamic light scattering. Currently, g2(τ) at a single point is readily accessible through high-frequency sampling of the intensity signal. However, two-dimensional wide-field imaging of g2(τ) is still limited by the cameras' frame rate. We propose and demonstrate a 2-pulse within-exposure modulation approach to break through the camera frame rate limit and obtain the quasi g2(τ) map in wide field with cameras of only ordinary frame rates.

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.

Dynamic light scattering and laser speckle contrast imaging of the brain: theory of the spatial and temporal statistics of speckle pattern evolution

Dynamic light scattering (DLS) and laser speckle contrast imaging (LSCI) are closely related techniques that exploit the statistics of speckle patterns, which can be utilized to measure cerebral blood flow (CBF). Conventionally, the temporal speckle intensity auto-correlation function g 2 t ( τ ) is calculated in DLS, while the spatial speckle contrast Ks is calculated in LSCI measurements. Due to the rapid development of CMOS detection technology with increased camera frame rates while still maintaining a large number of pixels, the ensemble or spatial average of g 2 s ( τ ) as well as the temporal contrast Kt can be easily calculated and utilized to quantify CBF. Although many models have been established, a proper summary is still lacking to fully characterize DLS and LSCI measurements for spatial and temporal statistics, laser coherence properties, various motion types, etc. As a result, there are many instances where theoretical models are misused. For instance, mathematical formulas derived in the diffusive regime or for ergodic systems are sometimes applied to small animal brain measurements, e.g., mice brains, where the assumptions are not valid. Therefore, we aim to provide a review of the speckle theory for both DLS and LSCI measurements with detailed derivations from first principles, taking into account non-ergodicity, spatial and temporal statistics of speckles, scatterer motion types, and laser coherence properties. From these calculations, we elaborate on the differences between spatial and temporal averaging for DLS and LSCI measurements that are typically ignored but can result in inaccurate measurements of blood flow, particularly the spatially varying nature of the static component in g 2 t ( τ ) and Kt. We also obtained g 2 s ( τ ) maps in in vivo mouse brain measurements using high frame rate CMOS cameras which have not been demonstrated before, and compared with g 2 t ( τ ) and Ks,t. This work provides a useful guide for choosing the correct model to analyze spatial and temporal speckle statistics in in-vivo DLS and LSCI measurements.

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.

Choosing a polarisation configuration for dynamic light scattering and laser speckle contrast imaging

Laser speckle contrast imaging (LSCI) is applied in various biomedical applications for full-field characterization of blood flow and tissue perfusion. The accuracy of the contrast interpretation and its conversion to the blood flow index depends on specific parameters of the optical system and scattering media. One such parameter is the polarisation of detected light, which is often adjusted to minimize specular reflections and image artefacts. The polarisation's effect on the detected light scattering dynamics and, therefore, the accuracy of LSCI data interpretation requires more detailed investigation. In this study, we used LSCI and Dynamic Light Scattering Imaging to evaluate the effects of the detected light polarisation when imaging perfusion in the mouse cortex. We found that cross-polarisation results in a shorter decorrelation time constant, a higher coherence degree and stronger dynamic scattering compared to the parallel-polarisation or no-polariser configurations. These results support the cross-polarisation configuration as the most optimal for brain cortex imaging and suggest against direct or calibrated comparisons between the contrast recordings made with different polarisation configurations.

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.

Diffuse Optical Monitoring of Cerebral Hemodynamics and Oxygen Metabolism during and after Cardiopulmonary Bypass: Hematocrit Correction and Neurological Vulnerability

Cardiopulmonary bypass (CPB) provides cerebral oxygenation and blood flow (CBF) during neonatal congenital heart surgery, but the impacts of CPB on brain oxygen supply and metabolic demands are generally unknown. To elucidate this physiology, we used diffuse correlation spectroscopy and frequency-domain diffuse optical spectroscopy to continuously measure CBF, oxygen extraction fraction (OEF), and oxygen metabolism (CMRO2) in 27 neonatal swine before, during, and up to 24 h after CPB. Concurrently, we sampled cerebral microdialysis biomarkers of metabolic distress (lactate-pyruvate ratio) and injury (glycerol). We applied a novel theoretical approach to correct for hematocrit variation during optical quantification of CBF in vivo. Without correction, a mean (95% CI) +53% (42, 63) increase in hematocrit resulted in a physiologically improbable +58% (27, 90) increase in CMRO2 relative to baseline at CPB initiation; following correction, CMRO2 did not differ from baseline at this timepoint. After CPB initiation, OEF increased but CBF and CMRO2 decreased with CPB time; these temporal trends persisted for 0-8 h following CPB and coincided with a 48% (7, 90) elevation of glycerol. The temporal trends and glycerol elevation resolved by 8-24 h. The hematocrit correction improved quantification of cerebral physiologic trends that precede and coincide with neurological injury following CPB.

Deep-learning-based separation of shallow and deep layer blood flow rates in diffuse correlation spectroscopy

Diffuse correlation spectroscopy faces challenges concerning the contamination of cutaneous and deep tissue blood flow. We propose a long short-term memory network to directly quantify the flow rates of shallow and deep-layer tissues. By exploiting the different contributions of shallow and deep-layer flow rates to auto-correlation functions, we accurately predict the shallow and deep-layer flow rates (RMSE = 0.047 and 0.034 ml/min/100 g of simulated tissue, R2 = 0.99 and 0.99, respectively) in a two-layer flow phantom experiment. This approach is useful in evaluating the blood flow responses of active muscles, where both cutaneous and deep-muscle blood flow increase with exercise.

SpeckleCam: high-resolution computational speckle contrast tomography for deep blood flow imaging

Laser speckle contrast imaging is widely used in clinical studies to monitor blood flow distribution. Speckle contrast tomography, similar to diffuse optical tomography, extends speckle contrast imaging to provide deep tissue blood flow information. However, the current speckle contrast tomography techniques suffer from poor spatial resolution and involve both computation and memory intensive reconstruction algorithms. In this work, we present SpeckleCam, a camera-based system to reconstruct high resolution 3D blood flow distribution deep inside the skin. Our approach replaces the traditional forward model using diffuse approximations with Monte-Carlo simulations-based convolutional forward model, which enables us to develop an improved deep tissue blood flow reconstruction algorithm. We show that our proposed approach can recover complex structures up to 6 mm deep inside a tissue-like scattering medium in the reflection geometry. We also conduct human experiments to demonstrate that our approach can detect reduced flow in major blood vessels during vascular occlusion.

Depth-sensitive diffuse speckle contrast topography for high-density mapping of cerebral blood flow in rodents

Significance

Frequent assessment of cerebral blood flow (CBF) is crucial for the diagnosis and management of cerebral vascular diseases. In contrast to large and expensive imaging modalities, such as nuclear medicine and magnetic resonance imaging, optical imaging techniques are portable and inexpensive tools for continuous measurements of cerebral hemodynamics. The recent development of an innovative noncontact speckle contrast diffuse correlation tomography (scDCT) enables three-dimensional (3D) imaging of CBF distributions. However, scDCT requires complex and time-consuming 3D reconstruction, which limits its ability to achieve high spatial resolution without sacrificing temporal resolution and computational efficiency.

Aim

We investigate a new diffuse speckle contrast topography (DSCT) method with parallel computation for analyzing scDCT data to achieve fast and high-density two-dimensional (2D) mapping of CBF distributions at different depths without the need for 3D reconstruction.

Approach

A new moving window method was adapted to improve the sampling rate of DSCT. A fast computation method utilizing MATLAB functions in the Image Processing Toolbox™ and Parallel Computing Toolbox™ was developed to rapidly generate high-density CBF maps. The new DSCT method was tested for spatial resolution and depth sensitivity in head-simulating layered phantoms and in-vivo rodent models.

Results

DSCT enables 2D mapping of the particle flow in the phantom at different depths through the top layer with varied thicknesses. Both DSCT and scDCT enable the detection of global and regional CBF changes in deep brains of adult rats. However, DSCT achieves fast and high-density 2D mapping of CBF distributions at different depths without the need for complex and time-consuming 3D reconstruction.

Conclusions

The depth-sensitive DSCT method has the potential to be used as a noninvasive, noncontact, fast, high resolution, portable, and inexpensive brain imager for basic neuroscience research in small animal models and for translational studies in human neonates.

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.

A 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.

Wide-field Intensity Fluctuation Imaging

The temporal intensity fluctuations contain important information about the light source and light-medium interaction and are typically characterized by the intensity autocorrelation function, g 2 ( τ ) . The measurement of g 2 ( τ ) is a central topic in many optical sensing applications, ranging from stellar intensity interferometer in astrophysics, to fluorescence correlation spectroscopy in biomedical sciences and blood flow measurement with dynamic light scattering. Currently, g 2 ( τ ) at a single point is readily accessible through high-frequency sampling of the intensity signal. However, two-dimensional wide-field measurement of g 2 ( τ ) is still limited by camera frame rates. We propose and demonstrate a 2-pulse within-exposure modulation approach to break through the camera frame rate limit and obtain the quasi g 2 ( τ ) map in wide field with cameras of only ordinary frame rates.

Fast time-domain diffuse correlation spectroscopy with superconducting nanowire single-photon detector: system validation and in vivo results

Time-domain diffuse correlation spectroscopy (TD-DCS) has been introduced as an advancement of the "classical" continuous wave DCS (CW-DCS) allowing one to not only to measure depth-resolved blood flow index (BFI) but also to extract optical properties of the measured medium without using any additional diffuse optics technique. However, this method is a photon-starved technique, specially when considering only the late photons that are of primary interest which has limited its in vivo application. In this work, we present a TD-DCS system based on a superconducting nanowire single-photon detector (SNSPD) with a high quantum efficiency, a narrow timing response, and a negligibly low dark count noise. We compared it to the typically used single-photon avalanche diode (SPAD) detector. In addition, this system allowed us to conduct fast in vivo measurements and obtain gated pulsatile BFI on the adult human forehead.

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 .

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.

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.

Interferometric diffusing wave spectroscopy imaging with an electronically variable time-of-flight filter

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.

Cerebrovascular Impedance as a Function of Cerebral Perfusion Pressure

Goal: Cerebrovascular impedance is modulated by a vasoactive autoregulative mechanism in response to changes in cerebral perfusion pressure. Characterization of impedance and the limits of autoregulation are important biomarkers of cerebral health. We developed a method to quantify impedance based on the spectral content of cerebral blood flow and volume at the cardiac frequency, measured with diffuse optical methods. Methods: In three non-human primates, we modulated cerebral perfusion pressure beyond the limits of autoregulation. Cerebral blood flow and volume were measured with diffuse correlation spectroscopy and near-infrared spectroscopy, respectively. Results: We show that impedance can be used to identify the lower and upper limits of autoregulation. Conclusions: This impedance method may be an alternative method to measure autoregulation and a way of assessing cerebral health non-invasively at the clinical bedside.

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.

Comparison of cerebral metabolic rate of oxygen, blood flow, and bispectral index under general anesthesia

Significance

The optical measurement of cerebral oxygen metabolism was evaluated.

Aim

Compare optically derived cerebral signals to the electroencephalographic bispectral index (BIS) sensors to monitor propofol-induced anesthesia during surgery.

Approach

Relative cerebral metabolic rate of oxygen (rCMRO 2 ) and blood flow (rCBF) were measured by time-resolved and diffuse correlation spectroscopies. Changes were tested against the relative BIS (rBIS) ones. The synchronism in the changes was also assessed by the R-Pearson correlation.

Results

In 23 measurements, optically derived signals showed significant changes in agreement with rBIS: during propofol induction, rBIS decreased by 67% [interquartile ranges (IQR) 62% to 71%],rCMRO 2 by 33% (IQR 18% to 46%), and rCBF by 28% (IQR 10% to 37%). During recovery, a significant increase was observed for rBIS (48%, IQR 38% to 55%),rCMRO 2 (29%, IQR 17% to 39%), and rCBF (30%, IQR 10% to 44%). The significance and direction of the changes subject-by-subject were tested: the coupling between the rBIS,rCMRO 2 , and rCBF was witnessed in the majority of the cases (14/18 and 12/18 for rCBF and 19/21 and 13/18 forrCMRO 2 in the initial and final part, respectively). These changes were also correlated in time ( R > 0.69 to R = 1 , p - values < 0.05 ).

Conclusions

Optics can reliably monitorrCMRO 2 in such conditions.

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.

Numerical approach to quantify depth-dependent blood flow changes in real-time using the diffusion equation with continuous-wave and time-domain diffuse correlation spectroscopy

Diffuse correlation spectroscopy (DCS) is a non-invasive optical technique that can measure brain perfusion by quantifying temporal intensity fluctuations of multiply scattered light. A primary limitation for accurate quantitation of cerebral blood flow (CBF) is the fact that experimental measurements contain information about both extracerebral scalp blood flow (SBF) as well as CBF. Separating CBF from SBF is typically achieved using multiple source-detector channels when using continuous-wave (CW) light sources, or more recently with use of time-domain (TD) techniques. Analysis methods that account for these partial volume effects are often employed to increase CBF contrast. However, a robust, real-time analysis procedure that can separate and quantify SBF and CBF with both traditional CW and TD-DCS measurements is still needed. Here, we validate a data analysis procedure based on the diffusion equation in layered media capable of quantifying both extra- and cerebral blood flow in the CW and TD. We find that the model can quantify SBF and CBF coefficients with less than 5% error compared to Monte Carlo simulations using a 3-layered brain model in both the CW and TD. The model can accurately fit data at a rate of <10 ms for CW data and <250 ms for TD data when using a least-squares optimizer.

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.

Imaging Dynamics Beneath Turbid Media via Parallelized Single-Photon Detection

Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such temporal correlation data to demonstrate deep-tissue video reconstruction of decorrelation dynamics. In this work, a single-photon avalanche diode array camera is utilized to simultaneously monitor the temporal dynamics of speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. Then a deep neural network is applied to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating tissue phantoms. The ability to reconstruct images of transient (0.1-0.4 s) dynamic events occurring up to 8 mm beneath a decorrelating tissue phantom with millimeter-scale resolution is demonstrated, and it is highlighted how the model can flexibly extend to monitor flow speed within buried phantom vessels.

Noninvasive hemoglobin sensing and imaging: optical tools for disease diagnosis

SIGNIFICANCE

Measurement and imaging of hemoglobin oxygenation are used extensively in the detection and diagnosis of disease; however, the applied instruments vary widely in their depth of imaging, spatiotemporal resolution, sensitivity, accuracy, complexity, physical size, and cost. The wide variation in available instrumentation can make it challenging for end users to select the appropriate tools for their application and to understand the relative limitations of different methods.

AIM

We aim to provide a systematic overview of the field of hemoglobin imaging and sensing.

APPROACH

We reviewed the sensing and imaging methods used to analyze hemoglobin oxygenation, including pulse oximetry, spectral reflectance imaging, diffuse optical imaging, spectroscopic optical coherence tomography, photoacoustic imaging, and diffuse correlation spectroscopy.

RESULTS

We compared and contrasted the ability of different methods to determine hemoglobin biomarkers such as oxygenation while considering factors that influence their practical application.

CONCLUSIONS

We highlight key limitations in the current state-of-the-art and make suggestions for routes to advance the clinical use and interpretation of hemoglobin oxygenation information.

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.

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.

Development of a Monte Carlo-wave model to simulate time domain diffuse correlation spectroscopy measurements from first principles

SIGNIFICANCE

Diffuse correlation spectroscopy (DCS) is an optical technique that measures blood flow non-invasively and continuously. The time-domain (TD) variant of DCS, namely, TD-DCS has demonstrated a potential to improve brain depth sensitivity and to distinguish superficial from deeper blood flow by utilizing pulsed laser sources and a gating strategy to select photons with different pathlengths within the scattering tissue using a single source-detector separation. A quantitative tool to predict the performance of TD-DCS that can be compared with traditional continuous wave DCS (CW-DCS) currently does not exist but is crucial to provide guidance for the continued development and application of these DCS systems.

AIMS

We aim to establish a model to simulate TD-DCS measurements from first principles, which enables analysis of the impact of measurement noise that can be utilized to quantify the performance for any particular TD-DCS system and measurement geometry.

APPROACH

We have integrated the Monte Carlo simulation describing photon scattering in biological tissue with the wave model that calculates the speckle intensity fluctuations due to tissue dynamics to simulate TD-DCS measurements from first principles.

RESULTS

Our model is capable of simulating photon counts received at the detector as a function of time for both CW-DCS and TD-DCS measurements. The effects of the laser coherence, instrument response function, detector gate delay, gate width, intrinsic noise arising from speckle statistics, and shot noise are incorporated in the model. We have demonstrated the ability of our model to simulate TD-DCS measurements under different conditions, and the use of our model to compare the performance of TD-DCS and CW-DCS under a few typical measurement conditions.

CONCLUSION

We have established a Monte Carlo-Wave model that is capable of simulating CW-DCS and TD-DCS measurements from first principles. In our exploration of the parameter space, we could not find realistic measurement conditions under which TD-DCS outperformed CW-DCS. However, the parameter space for the optimization of the contrast to noise ratio of TD-DCS is large and complex, so our results do not imply that TD-DCS cannot indeed outperform CW-DCS under different conditions. We made our code available publicly for others in the field to find use cases favorable to TD-DCS. TD-DCS also provides a promising way to measure deep brain tissue dynamics using a short source-detector separation, which will benefit the development of technologies including high density DCS systems and image reconstruction using a limited number of source-detector pairs.

Non-Invasive Blood Flow Speed Measurement Using Optics

Non-invasive measurement of the arterial blood speed gives important health information such as cardio output and blood supplies to vital organs. The magnitude and change in arterial blood speed are key indicators of the health conditions and development and progression of diseases. We demonstrated a simple technique to directly measure the blood flow speed in main arteries based on the diffused light model. The concept is demonstrated with a phantom that uses intralipid hydrogel to model the biological tissue and an embedded glass tube with flowing human blood to model the blood vessel. The correlation function of the measured photocurrent was used to find the electrical field correlation function via the Siegert relation. We have shown that the characteristic decorrelation rate (i.e., the inverse of the decoherent time) is linearly proportional to the blood speed and independent of the tube diameter. This striking property can be explained by an approximate analytic solution for the diffused light equation in the regime where the convective flow is the dominating factor for decorrelation. As a result, we have demonstrated a non-invasive method of measuring arterial blood speed without any prior knowledge or assumption about the geometric or mechanic properties of the blood vessels.

Monte Carlo analysis of optical heart rate sensors in commercial wearables: the effect of skin tone and obesity on the photoplethysmography (PPG) signal

Commercially available wearable devices have been used for fitness and health management and their demand has increased over the last ten years. These "general wellness" and heart-rate monitoring devices have been cleared by the Food and Drug Administration for over-the-counter use, yet anecdotal and more systematic reports seem to indicate that their error is higher when used by individuals with elevated skin tone and high body mass index (BMI). In this work, we used Monte Carlo modeling of a photoplethysmography (PPG) signal to study the theoretical limits of three different wearable devices (Apple Watch series 5, Fitbit Versa 2 and Polar M600) when used by individuals with a BMI range of 20 to 45 and a Fitzpatrick skin scale 1 to 6. Our work shows that increased BMI and skin tone can induce a relative loss of signal of up to 61.2% in Fitbit versa 2, 32% in Apple S5 and 32.9% in Polar M600 when considering the closest source-detector pair configuration in these devices.

Adaptive processing for noise attenuation in laser speckle contrast imaging

BACKGROUND AND OBJECTIVE

Blood vessel visualization is an essential task to treat and evaluate diseases such as port-wine stain. Laser Speckle Contrast Imaging (LSCI) have applications in the analysis of the microvasculature. However, it is often limited to superficial depths because the tissue among skin and microvasculature introduces noise in the image. To analyze microvasculature, traditional LSCI methods compute a Contrast Image (CI) by using a shifting window of fixed size and shape, which is inadequate in images with structures different types of morphologies in it, as happens in LSCI. This work aims to reduce the noise in the CIs to improve the visualization of blood vessels at high depths (> 300 μ m).

METHODS

The proposed method processes the CIs with analysis windows that change their size and shape for each pixel to compute the contrast representation with pixels more representatives to the region.

RESULTS

We performed experiments varying the depth of the blood vessels, the number of frames required to compute the representation, and the blood flow in the blood vessel. We looked for an improvement in the Contrast to Noise Ratio (CNR) in the periphery of the blood vessels using an analysis of variance. Finding that the adaptive processing of the contrast images allows a significant noise attenuation, translated into a better visualization of blood vessels. An average CNR of 2.62 ± 1 and 5.26 ± 1.7 was reached for in-vitro and in-vivo tests respectively, which is higher in comparison with traditional LSCI approaches.

CONCLUSIONS

The results, backed by the measured CNR, obtained a noise reduction in the CIs, this means a better temporal and spatial resolution. The proposed awK method can obtain an image with better quality than the state-of-the-art methods using fewer frames.