Multi-speckle diffuse correlation spectroscopy to measure cerebral blood flow

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.

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.

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.

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.

Laser speckle simulation tool based on stochastic differential equations for bio imaging applications

Laser speckle-based blood flow imaging is a well-accepted and widely used method for pre-clinical and clinical applications. Although it was introduced as a method to measure only superficial blood flow (< 1mm depth), several recently introduced variants resulted in measuring deep tissue blood flow (a few cm) as well. A means of simulating laser speckles is often necessary for the analysis and development of these imaging modalities, as evident from many such attempts towards developing simulation tools in the past. Such methods often employ Fourier transforms or statistical tools to simulate speckles with desired statistical properties. We present the first method to use a stochastic differential equation to generate laser speckles with a pre-determined probability density function and a temporal auto-correlation. The method allows the choice of apriori gamma distribution along with simple exponential or more complex temporal auto-correlation statistics for simulated speckles, making it suitable for different blood flow profiles. In contrast to the existing methods that often generate speckles associated with superficial flow, we simulate both superficial and diffuse speckles leading to applications in deep tissue blood flow imaging. In addition, we have also incorporated appropriate models for noise associated with the detectors to simulate realistic speckles. We have validated our model by comparing the simulated speckles with those obtained from in-vivo studies in mice and healthy human subject.

High-density diffuse correlation tomography with enhanced depth localization and minimal surface artefacts

A spatially weighted filter applied to both the measurement and the Jacobian is proposed for high-density diffuse correlation tomography (DCT) to remove unwanted extracerebral interferences and artefacts along with better depth localization in the reconstructed blood flow images. High-density DCT is implemented by appropriate modification of recently introduced Multi-speckle Diffuse Correlation Spectroscopy (M-DCS) system. Additionally, we have used autocorrelation measurements at multiple delay-times in an iterative manner to improve the reconstruction results. The proposed scheme has been validated by simulations, phantom experiments and in-vivo human experiments.

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.

A simple algorithm for diffuse optical tomography without Jacobian inversion

A computationally simpler algorithm to reconstruct the optical property distribution of turbid media using diffuse optical tomographic principles is presented. The proposed algorithm eliminates the requirement of large Jacobian matrix inversion which otherwise is essential for tomographic imaging. The most significant Jacobians are identified based on proper thresholding of the measurement and the intersection of these Jacobians gives the approximate spatial location of the inhomogeneity. The algorithm is tested and optimized using simulations and further validated using tissue-mimicking phantom-based experiments andin-vivosmall-animal experiments.

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.

Multi-exposure interferometric diffusing wave spectroscopy

We present multi-exposure interferometric diffusing wave spectroscopy (MiDWS), which measures brain blood flow index (BFI) continuously and non-invasively. MiDWS employs interferometry to detect low light levels, probing the optical field autocorrelation indirectly by varying the sensor exposure time. Here MiDWS is compared with conventional interferometric diffusing wave spectroscopy and speckle contrast optical spectroscopy in phantoms. Notably, the MiDWS approach enables the use of low frame rate, two-dimensional complementary metal-oxide semiconductor cameras in a short exposure time regime, where detector noise greatly exceeds the sample photon count. Finally, we show that MiDWS can monitor the BFI simultaneously at two source-collector separations (1 and 3 cm) on the adult human head on a single camera, enabling the use of superficial signal regression techniques to improve brain specificity.