Speckle contrast optical spectroscopy (SCOS) allows for simultaneous monitoring of blood flow and volume changes within each cardiac pulse. SCOS measurements were collected from 10 subjects at two time points and the blood flow and volume pulse waveforms (PWFs) were extracted. The eXtreme Gradient Boosting model was trained on an individual subject’s first measurement and predicted BP for that subject’s second measurement. A model trained on features extracted from both flow and volume PWFs was compared to a model trained only on features extracted from blood volume PWFs. With the addition of blood flow information, BP estimation was significantly improved.
KEYWORDS: Single photon avalanche diodes, Field programmable gate arrays, Cameras, Signal to noise ratio, Photons, Actinium, Autocorrelation, Principal component analysis, Data compression, Diffusers
SignificanceDiffuse correlation spectroscopy (DCS) is an indispensable tool for quantifying cerebral blood flow noninvasively by measuring the autocorrelation function (ACF) of the diffused light. Recently, a multispeckle DCS approach was proposed to scale up the sensitivity with the number of independent speckle measurements, leveraging the rapid development of single-photon avalanche diode (SPAD) cameras. However, the extremely high data rate from advanced SPAD cameras is beyond the data transfer rate commonly available and requires specialized high-performance computation to calculate large number of autocorrelators (ACs) for real-time measurements.AimWe aim to demonstrate a data compression scheme in the readout field-programmable gate array (FPGA) of a large-pixel-count SPAD camera. On-FPGA, data compression should democratize SPAD cameras and streamline system integration for multispeckle DCS.ApproachWe present a 192 × 128 SPAD array with 128 linear ACs embedded on an FPGA to calculate 12,288 ACFs in real time.ResultsWe achieved a signal-to-noise ratio (SNR) gain of 110 over a single-pixel DCS system and more than threefold increase in SNR with respect to the state-of-the-art multispeckle DCS.ConclusionsThe FPGA-embedded autocorrelation algorithm offers a scalable data compression method to large SPAD array, which can improve the sensitivity and usability of multispeckle DCS instruments.
Speckle contrast optical spectroscopy (SCOS) allows for simultaneous monitoring of blood flow and volume changes within each cardiac pulse. SCOS measurements were collected from 13 subjects and blood flow and volume pulse waveforms (PWFs) were extracted. The correlations between features extracted from the PWFs and blood pressure before and after an exercise activity were investigated. We found that the time delay between the blood flow and volume peaks was strongly correlated with changes in blood pressure (R = -0.73), suggesting that the combination of blood flow and volume information may improve blood pressure estimation.
KEYWORDS: Monte Carlo methods, Photons, Scattering, Speckle, Sensors, Signal to noise ratio, Performance modeling, Tissues, Blood circulation, Tissue optics
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.
KEYWORDS: Signal detection, Hemodynamics, Spectroscopy, Monte Carlo methods, Brain, Blood circulation, Scattering, Time metrology, Neurophotonics, Neurons
Significance: Diffuse correlation spectroscopy (DCS) measures cerebral blood flow non-invasively. Variations in blood flow can be used to detect neuronal activities, but its peak has a latency of a few seconds, which is slow for real-time monitoring. Neuronal cells also deform during activation, which, in principle, can be utilized to detect neuronal activity on fast timescales (within 100 ms) using DCS.
Aims: We aim to characterize DCS signal variation quantified as the change of the decay time of the speckle intensity autocorrelation function during neuronal activation on both fast (within 100 ms) and slow (100 ms to seconds) timescales.
Approach: We extensively modeled the variations in the DCS signal that are expected to arise from neuronal activation using Monte Carlo simulations, including the impacts of neuronal cell motion, vessel wall dilation, and blood flow changes.
Results: We found that neuronal cell motion induces a DCS signal variation of ∼10 − 5. We also estimated the contrast and number of channels required to detect hemodynamic signals at different time delays.
Conclusions: From this extensive analysis, we do not expect to detect neuronal cell motion using DCS in the near future based on current technology trends. However, multi-channel DCS will be able to detect hemodynamic response with sub-second latency, which is interesting for brain–computer interfaces.
Laser Speckle Contrast Imaging (LSCI) measurements provide sufficient information on the changes in the tissue dynamics using spatial contrast measurements over an integration time. To allow the adoption of LSCI in humans, we propose a fiber-based LSCI system that has the potential to overcome free-space imaging of Speckle Contrast Optical Tomography (SCOT) while maintaining the high-speed imaging of LSCI. Here, we propose Dynamic Speckle Model (DSM) to develop the noise model for fiber-based LSCI (fb-LSCI) taking into account all the noise sources. We have identified operating parameter space i.e. small speckle to pixel ratio and long exposure time to minimise the impact of noise sources on the contrast measured. The performance of fb-LSCI is compared with other methods that measure changes in tissue dynamics such as DCS.
Cerebral blood flow is an important biomarker of brain health and function, as it regulates the delivery of oxygen and substrates to tissue and the removal of metabolic waste products. Diffuse Correlation Spectroscopy (DCS) is a promising noninvasive optical technique for monitoring cerebral blood flow and for measuring cortex functional activation tasks. However, the current state-of-the-art DCS adoption is hindered by a trade-off between sensitivity to the cortex and signal-to-noise ratio (SNR). Here we report on a multi-speckle DCS (mDCS) system based on a 1024-pixel single-photon avalanche diode (SPAD) camera that removes this trade-off and demonstrated a 32-fold increase in SNR with respect to traditional single-speckle DCS.
Significance: Cerebral blood flow is an important biomarker of brain health and function as it regulates the delivery of oxygen and substrates to tissue and the removal of metabolic waste products. Moreover, blood flow changes in specific areas of the brain are correlated with neuronal activity in those areas. Diffuse correlation spectroscopy (DCS) is a promising noninvasive optical technique for monitoring cerebral blood flow and for measuring cortex functional activation tasks. However, the current state-of-the-art DCS adoption is hindered by a trade-off between sensitivity to the cortex and signal-to-noise ratio (SNR).
Aim: We aim to develop a scalable method that increases the sensitivity of DCS instruments.
Approach: We report on a multispeckle DCS (mDCS) approach that is based on a 1024-pixel single-photon avalanche diode (SPAD) camera. Our approach is scalable to >100,000 independent speckle measurements since large-pixel-count SPAD cameras are becoming available, owing to the investments in LiDAR technology for automotive and augmented reality applications.
Results: We demonstrated a 32-fold increase in SNR with respect to traditional single-speckle DCS.
Conclusion: A mDCS system that is based on a SPAD camera serves as a scalable method toward high-sensitivity DCS measurements, thus enabling both high sensitivity to the cortex and high SNR.
Atomically thin transition-metal dichalcogenide (TMD) semiconductors possess strong Coulomb interactions due to reduced dielectric screening, leading to the formation of excitons with exceptionally large binding energies. The enhanced stability of excitons in these materials provides a unique platform to investigate excitonic interactions at room temperature and to examine the role of plasma effects and excitonic interactions over a broad range of excitation densities.
We report an excitation-density dependent crossover between two regimes: Using ultrafast absorption spectroscopy, we observe a pronounced red shift of the exciton resonance followed by an anomalous blue shift with increasing excitation density. Using both material-realistic computation and phenomenological modeling, we attribute this observation to long-range Coulomb interaction in the presence of plasma screening in an attraction-repulsion crossover with the short-ranged exciton-exciton interaction that mimics the Lennard-Jones potential between atoms, suggesting a strong analogy between excitons and atoms in respect of inter-particle interaction.
Our findings underline the important role of many-particle renormalizations and screening due to excited carriers in the device-relevant regime of optically or electrically excited TMDs.
Monolayer transition-metal dichalcogenides such as MoS2 and WS2 are prime examples of atomically thin semiconducting crystals that exhibit remarkable electronic and optical properties. They have a pair of valleys that can serve as a new electronic degree of freedom, and these valleys obey optical selection rules with circularly polarized light. Here, we discuss how ultrafast laser pulses can be used to tune their energy levels in a controllable valley-selective manner. The energy tunability is extremely large, comparable to what would be obtained using a hundred Tesla of magnetic field. We will also show that such valley tunability can be performed while we effectively manipulate the valley selection rules. Finally, we will explore the prospect of using this technique through photoemission spectroscopy to create a new phase of matter called a valley Floquet topological insulator.
Semiconductors that are atomically thin can exhibit novel optical properties beyond those encountered in the bulk compounds. Monolayer transition-metal dichalcogenides (TMDs) are leading examples of such semiconductors that possess remarkable optical properties. They obey unique selection rules where light with different circular polarization can be used for selective photoexcitation at two different valleys in the momentum space. These valleys constitute bandgaps that are normally locked in the same energy. Selectively varying their energies is of great interest for applications because it unlocks the potential to control valley degree of freedom, and offers a new promising way to carry information in next-generation valleytronics. In this proceeding paper, we show that the energy gaps at the two valleys can be shifted relative to each other by means of the optical Stark effect in a controllable valley-selective manner. We discuss the physics of the optical Stark effect, and we describe the mechanism that leads to its valleyselectivity in monolayer TMD tungsten disulfide (WS2).
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