Simultaneously extracting multiple parameters via fitting one single autocorrelation function curve in diffuse correlation spectroscopy

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.

Comparison of diffuse correlation spectroscopy analytical models for measuring cerebral blood flow in adults

Significance

Although multilayer analytical models have been proposed to enhance brain sensitivity of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow, the traditional homogeneous model remains dominant in clinical applications. Rigorous in vivo comparison of these analytical models is lacking.

Aim

We compare the performance of different analytical models to estimate a cerebral blood flow index (CBFi) with DCS in adults.

Approach

Resting-state data were obtained on a cohort of 20 adult patients with subarachnoid hemorrhage. Data at 1 and 2.5 cm source-detector separations were analyzed with the homogenous, two-layer, and three-layer models to estimate scalp blood flow index and CBFi. The performance of each model was quantified via fitting convergence, fit stability, brain-to-scalp flow ratio (BSR), and correlation with transcranial Doppler ultrasound (TCD) measurements of cerebral blood flow velocity in the middle cerebral artery (MCA).

Results

The homogeneous model has the highest pass rate (100%), lowest coefficient of variation (CV) at rest (median [IQR] at 1 Hz of 0.18 [0.13, 0.22]), and most significant correlation with MCA blood flow velocities (R s = 0.59 , p = 0.010 ) compared with both the two- and three-layer models. The multilayer model pass rate was significantly correlated with extracerebral layer thicknesses. Discarding datasets with non-physiological BSRs increased the correlation between DCS measured CBFi and TCD measured MCA velocities for all models.

Conclusions

We found that the homogeneous model has the highest pass rate, lowest CV at rest, and most significant correlation with MCA blood flow velocities. Results from the multilayer models should be taken with caution because they suffer from lower pass rates and higher coefficients of variation at rest and can converge to non-physiological values for CBFi. Future work is needed to validate these models in vivo, and novel approaches are merited to improve the performance of the multimodel models.

Correlation and asynchronization of electroencephalogram and cerebral blood flow in active and passive stimulations

Objective.Real-time brain monitoring is of importance for intraoperative surgeries and intensive care unit, in order to take timely clinical interventions. Electroencephalogram (EEG) is a conventional technique for recording neural excitations (e.g. brain waves) in the cerebral cortex, and near infrared diffuse correlation spectroscopy (DCS) is an emerging technique that can directly measure the cerebral blood flow (CBF) in microvasculature system. Currently, the relationship between the neural activities and cerebral hemodynamics that reflects the vasoconstriction features of cerebral vessels, especially under both active and passive situation, has not been elucidated thus far, which triggers the motivation of this study.Approach.We used the verbal fluency test as an active cognitive stimulus to the brain, and we manipulated blood pressure changes as a passive challenge to the brain. Under both protocols, the CBF and EEG responses were longitudinally monitored throughout the cerebral stimulus. Power spectrum approaches were applied the EEG signals and compared with CBF responses.Main results.The results show that the EEG response was significantly faster and larger in amplitude during the active cognitive task, when compared to the CBF, but with larger individual variability. By contrast, CBF is more sensitive when response to the passive task, and with better signal stability. We also found that there was a correlation (p< 0.01,r= 0.866,R2= 0.751) between CBF and EEG in initial response during the active task, but no significant correlation (p> 0.05) was found during the passive task. The similar relations were also found between regional brain waves and blood flow.Significance.The asynchronization and correlation between the two measurements indicates the necessity of monitoring both variables for comprehensive understanding of cerebral physiology. Deep exploration of their relationships provides promising implications for DCS/EEG integration in the diagnosis of various neurovascular and psychiatric diseases.

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Two-layer analytical model for estimation of layer thickness and flow using Diffuse Correlation Spectroscopy

Diffuse correlation spectroscopy (DCS) has been widely explored for its ability to measure cerebral blood flow (CBF), however, mostly under the assumption that the human head is homogenous. In addition to CBF, knowledge of extracerebral layers, such as skull thickness, can be informative and crucial for patient with brain complications such as traumatic brain injuries. To bridge the gap, this study explored the feasibility of simultaneously extracting skull thickness and flow in the cortex layer using DCS. We validated a two-layer analytical model that assumed the skull as top layer with a finite thickness and the brain cortex as bottom layer with semi-infinite geometry. The model fitted for thickness of the top layer and flow of the bottom layer, while assumed other parameters as constant. The accuracy of the two-layer model was tested against the conventional single-layer model using measurements from custom made two-layer phantoms mimicking skull and brain. We found that the fitted top layer thickness at each source detector (SD) distance is correlated with the expected thickness. For the fitted bottom layer flow, the two-layer model fits relatively consistent flow across all top layer thicknesses. In comparison, the conventional one-layer model increasingly underestimates the bottom layer flow as top layer thickness increases. The overall accuracy of estimating first layer thickness and flow depends on the SD distance in relationship to first layer thickness. Lastly, we quantified the influence of uncertainties in the optical properties of each layer. We found that uncertainties in the optical properties only mildly influence the fitted thickness and flow. In this work we demonstrate the feasibility of simultaneously extracting of layer thickness and flow using a two-layer DCS model. Findings from this work may introduce a robust and cost-effective approach towards simultaneous bedside assessment of skull thickness and cerebral blood flow.

Blood flow estimation via numerical integration of temporal autocorrelation function in diffuse correlation spectroscopy

BACKGROUND AND OBJECTIVE

Diffuse correlation spectroscopy (DCS) is an optical technique widely used to monitor blood flow. Recently, efforts have been made to derive new signal processing methods to minimize the systems used and shorten the signal processing time. Herein, we propose alternative approaches to obtain blood flow information via DCS by numerically integrating the temporal autocorrelation curves.

METHODS

We use the following methods: the inverse of K² (IK2)-based on the framework of diffuse speckle contrast analysis-and the inverse of the numerical integration of squared g₁ (INISg1) which, based on the normalized electric field autocorrelation curve, is more simplified than IK2. In addition, g₁ thresholding is introduced to further reduce computational time and make the suggested methods comparable to the conventional nonlinear fitting approach. To validate the feasibility of the suggested methods, studies using simulation, liquid phantom, and in vivo settings were performed. In the meantime, the suggested methods were implemented and tested on three types of Arduino (Arduino Due, Arduino Nano 33 BLE Sense, and Portenta H7) to demonstrate the possibility of miniaturizing the DCS systems using microcotrollers for signal processing.

RESULTS

The simulation and experimental results confirm that both IK2 and INISg1 are sufficiently relevant to capture the changes in blood flow information. More interestingly, when g₁ thresholding was applied, our results showed that INISg1 outperformed IK2. It was further confirmed that INISg1 with g₁ thresholding implemented on a PC and Portenta H7, an advanced Arduino board, performed faster than did the deep learning-based, state-of-the-art processing method.

CONCLUSION

Our findings strongly indicate that INISg1 with g₁ thresholding could be an alternative approach to derive relative blood flow information via DCS, which may contribute to the simplification of DCS methodologies.

Extraction of tissue optical property and blood flow from speckle contrast diffuse correlation tomography (scDCT) measurements

Measurement of blood flow in tissue provides vital information for the diagnosis and therapeutic monitoring of various vascular diseases. A noncontact, camera-based, near-infrared speckle contrast diffuse correlation tomography (scDCT) technique has been recently developed for 3D imaging of blood flow index (αD_B) distributions in deep tissues up to a centimeter. A limitation with the continuous-wave scDCT measurement of blood flow is the assumption of constant and homogenous tissue absorption coefficient (μ _a ). The present study took the advantage of rapid, high-density, noncontact scDCT measurements of both light intensities and diffuse speckle contrast at multiple source-detector distances and developed two-step fitting algorithms for extracting both μ _a and αD_B. The new algorithms were tested in tissue-simulating phantoms with known optical properties and human forearms. Measurement results were compared against established near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) techniques. The accuracies of our new fitting algorithms with scDCT measurements in phantoms (up to 16% errors) and forearms (up to 23% errors) are comparable to relevant study results (up to 25% errors). Knowledge of μ _a not only improved the accuracy in calculating αD_B but also provided the potential for quantifying tissue blood oxygenation via spectral measurements. A multiple-wavelength scDCT system with new algorithms is currently developing to fit multi-wavelength and multi-distance data for 3D imaging of both blood flow and oxygenation distributions in deep tissues.

Microvascular blood flow changes of the abductor pollicis brevis muscle during sustained static exercise

A practical assessment of the general health and microvascular function of the palm muscle, abductor pollicis brevis (APB), is important for the diagnosis of different conditions. In this study, we have developed a protocol and a probe to study microvascular blood flow using near-infrared diffuse correlation spectroscopy (DCS) in APB during and after thumb abduction at 55% of maximum voluntary contraction (MVC). Near-infrared time resolved spectroscopy (TRS) was also used to characterize the baseline optical and hemodynamic properties. Thirteen (n=13) subjects were enrolled and subdivided in low MVC (N=6, MVC<2.3 kg) and high MVC (N=7, MVC≥2.3 kg) groups. After ruling out significant changes in the systemic physiology that influence the muscle hemodynamics, we have observed that the high MVC group showed a 56% and 36% decrease in the blood flow during exercise, with respect to baseline, in the long and short source-detector (SD) separations (p=0.031 for both). No statistical differences were shown for the low MVC group (p=1 for short and p=0.15 for long SD). These results suggest that the mechanical occlusion, due to increased intramuscular pressure, exceeded the vasodilation elicited by the higher metabolic demand. Also, blood flow changes during thumb contraction negatively correlated (R=-0.7, p<0.01) with the absolute force applied by each subject. Furthermore, after the exercise, muscular blood flow increased significantly immediately after thumb contractions in both high and low MVC groups, with respect to the recorded values during the exercise (p=0.031). An increase of 251% (200%) was found for the long (short) SD in the low MVC group. The high MVC groups showed a significant 90% increase in blood flow only after 80 s from the start of the protocol. For both low and high MVC groups, blood flow recovered to baseline values within 160 s from starting the exercise. In conclusion, DCS allows the study of the response of a small muscle to static exercise and can be potentially used in multiple clinical conditions scenarios for assessing microvascular health.

Quantification of blood flow index in diffuse correlation spectroscopy using long short-term memory architecture

Diffuse correlation spectroscopy (DCS) is a noninvasive technique that derives blood flow information from measurements of the temporal intensity fluctuations of multiply scattered light. Blood flow index (BFI) and especially its variation was demonstrated to be approximately proportional to absolute blood flow. We investigated and assessed the utility of a long short-term memory (LSTM) architecture for quantification of BFI in DCS. Phantom and in vivo experiments were established to measure normalized intensity autocorrelation function data. Improved accuracy and faster computational time were gained by the proposed LSTM architecture. The results support the notion of using proposed LSTM architecture for quantification of BFI in DCS. This approach would be especially useful for continuous real-time monitoring of blood flow.

Fourier domain diffuse correlation spectroscopy with heterodyne holographic detection

We present a new approach to diffuse correlation spectroscopy which overcomes the limited light throughput of single-mode photon counting techniques. Our system employs heterodyne holographic detection to allow parallel measurement of the power spectrum of a fluctuating electric field across thousands of modes, at the shot noise limit, using a conventional sCMOS camera. This yields an order of magnitude reduction in detector cost compared to conventional techniques, whilst also providing robustness to the effects of ambient light and an improved signal-to-noise ratio during in vitro experiments. We demonstrate a GPU-accelerated holographic demodulation system capable of processing the incoming data (79.4 M pixels per second) in real-time, and a novel Fourier domain model of diffuse correlation spectroscopy which permits the direct recovery of flow parameters from the measured data. Our detection and modelling strategy are rigorously validated by modulating the Brownian component of an optical tissue phantom, demonstrating absolute measurements of the Brownian diffusion coefficient in excellent agreement with conventional methods. We further demonstrate the feasibility of our system through in vivo measurement of pulsatile flow rates measured in the human forearm.

Noncontact Speckle Contrast Diffuse Correlation Tomography of Blood Flow Distributions in Burn Wounds: A Preliminary Study

INTRODUCTION

Tissue injuries are often associated with abnormal blood flow (BF). The ability to assess BF distributions in injured tissues enables objective evaluation of interventions and holds the potential to improve the acute management of these injuries on battlefield.

MATERIALS AND METHODS

We have developed a novel speckle contrast diffuse correlation tomography (scDCT) system for noncontact 3D imaging of tissue BF distributions. In scDCT, a galvo mirror was used to remotely project near-infrared point light to different source positions and an electron multiplying charge-coupled-device was used to detect boundary diffuse speckle contrasts. The normalized boundary data were then inserted into a modified Near-Infrared Fluorescence and Spectral Tomography program for 3D reconstructions of BF distributions. This article reports the first application of scDCT for noncontact 3D imaging of BF distributions in burn wounds.

RESULTS

Significant lower BF values were observed in the burned areas/volumes compared to surrounding normal tissues.

CONCLUSIONS

The unique noncontact 3D imaging capability makes the scDCT applicable for intraoperative assessment of burns/wounds, without risk of infection and without interfering with sterility of the surgical field. The portable scDCT device holds the potential to be used by surgeons in combat surgical hospitals to improve the acute management of battlefield burn injuries.

Enhancement of diffuse correlation spectroscopy tissue blood flow measurement by acoustic radiation force

The current research on acousto-optic effects focuses on the interactions of acoustic waves with static optical properties rather than dynamic features such as tissue blood flow. Diffuse correlation spectroscopy (DCS) is an emerging technology capable of direct measurements of tissue blood flow by probing the movements of red blood cells (RBCs). In this article, we investigated the relations between the acoustic radiation force (ARF) and ultrasonic patterns by the finite element simulations. Based on the outcomes, we experimentally explored how the ultrasound-generated ARF enhance the DCS data as well as the blood flow measurements. The results yield the optimal pattern to generate ARF and elucidate the relations between the ultrasonic emission and flow elevations. The flow modality combing the DCS with ARF modulations, which was proposed in this study for the first time, would promote disease diagnosis and therapeutic assessment in the situation wherein the blood flow contrast between healthy and pathological tissues is insufficient.

Accuracy and precision of tissue optical properties and hemodynamic parameters estimated by the BabyLux device: a hybrid time-resolved near-infrared and diffuse correlation spectroscopy neuro-monitor

We have investigated the accuracy and precision of "the BabyLux device", a hybrid time-resolved near-infrared (TRS) and diffuse correlation spectroscopy (DCS) neuro-monitor for the pre-term infant. Numerical data with realistic noise were simulated and analyzed using the BabyLux device as a reference system and different experimental and analysis parameters. The results describe the limits for the precision and the accuracy to be expected. The dependence of these limits on different experimental conditions and choices of the analysis method is also described. Experiments demonstrate comparable values for precision with respect to the simulation results.

Approaches to denoise the diffuse optical signals for tissue blood flow measurement

Various diseases are relevant to the abnormal blood flow in tissue. Diffuse correlation spectroscopy (DCS) is an emerging technology to extract the blood flow index (BFI) from light electric field temporal autocorrelation data. To account for tissue heterogeneity and irregular geometry, we developed an innovative DCS algorithm (i.e., the Nth order linear algorithm, or simply the NL algorithm) previously, in which the DCS signals are fully utilized through iterative linear regressions. Under the framework of NL algorithm, the BFI to be extracted is significantly influenced by the linear regression approach adopted. In this study, three approaches were proposed and evaluated for performing the iterative linear regressions, in order to understand what are the appropriate regression methods for BFI estimation. The three methods are least-squared minimization (L₂ norm), least-absolute minimization (L₁ norm) and support vector regression (SVR), where L₂ norm is a conventional approach to perform linear regression. L₁ norm and SVR are the approaches newly introduced here to process the DCS data. Computer simulations and the autocorrelation data collected from liquid phantom and human tissues are utilized to evaluate the three approaches. The results show that the best performance is achieved by the SVR approach in extracting the BFI values, with an error rate of 2.23% at 3.0 cm source-detector separation. The L₁ norm method gives a medium error of 2.81%. In contrast, the L₂ norm method leads to the largest error (3.93%) in extracting the BFI values. The outcomes derived from this study will be very helpful for the tissue blood flow measurements, which is critical for translating the DCS technology to the clinic.

Prolonged monitoring of cerebral blood flow and autoregulation with diffuse correlation spectroscopy in neurocritical care patients

Monitoring of cerebral blood flow (CBF) and autoregulation are essential components of neurocritical care, but continuous noninvasive methods for CBF monitoring are lacking. Diffuse correlation spectroscopy (DCS) is a noninvasive diffuse optical modality that measures a CBF index ( CBF i ) in the cortex microvasculature by monitoring the rapid fluctuations of near-infrared light diffusing through moving red blood cells. We tested the feasibility of monitoring CBF i with DCS in at-risk patients in the Neurosciences Intensive Care Unit. DCS data were acquired continuously for up to 20 h in six patients with aneurysmal subarachnoid hemorrhage, as permitted by clinical care. Mean arterial blood pressure was recorded synchronously, allowing us to derive autoregulation curves and to compute an autoregulation index. The autoregulation curves suggest disrupted cerebral autoregulation in most patients, with the severity of disruption and the limits of preserved autoregulation varying between subjects. Our findings suggest the potential of the DCS modality for noninvasive, long-term monitoring of cerebral perfusion, and autoregulation.

Development and characterization of a multidistance and multiwavelength diffuse correlation spectroscopy system

This paper presents a multidistance and multiwavelength diffuse correlation spectroscopy (DCS) approach and its implementation to simultaneously measure the optical proprieties of deep tissue as well as the blood flow. The system consists of three long coherence length lasers at different wavelengths in the near-infrared, eight single-photon detectors, and a correlator board. With this approach, we collect both light intensity and DCS data at multiple distances and multiple wavelengths, which provide unique information to fit for all the parameters of interest: scattering, blood flow, and hemoglobin concentration. We present the characterization of the system and its validation with phantom measurements.

Simultaneously extracting multiple parameters via multi-distance and multi-exposure diffuse speckle contrast analysis

Recent advancements in diffuse speckle contrast analysis (DSCA) have opened the path for noninvasive acquisition of deep tissue microvasculature blood flow. In fact, in addition to blood flow index αD_B , the variations of tissue optical absorption μ_a , reduced scattering coefficients [Formula: see text], as well as coherence factor β can modulate temporal fluctuations of speckle patterns. In this study, we use multi-distance and multi-exposure DSCA (MDME-DSCA) to simultaneously extract multiple parameters such as μ_a , [Formula: see text], αD_B , and β. The validity of MDME-DSCA has been validated by the simulated data and phantoms experiments. Moreover, as a comparison, the results also show that it is impractical to simultaneously obtain multiple parameters by multi-exposure DSCA (ME-DSCA).

Diffuse optical measurements of head and neck tumor hemodynamics for early prediction of chemoradiation therapy outcomes

This study used a hybrid near-infrared diffuse optical instrument to monitor tumor hemodynamic responses to chemoradiation therapy for early prediction of treatment outcomes in patients with head and neck cancer. Forty-seven patients were measured once per week to evaluate the hemodynamic status of clinically involved cervical lymph nodes as surrogates for the primary tumor response. Patients were classified into two groups: complete response (CR) (n=29) and incomplete response (IR) (n=18). Tumor hemodynamic responses were found to be associated with clinical outcomes (CR/IR), wherein the associations differed depending on human papillomavirus (HPV-16) status. In HPV-16 positive patients, significantly lower levels in tumor oxygenated hemoglobin concentration ([HbO2]) at weeks 1 to 3, total hemoglobin concentration at week 3, and blood oxygen saturation (StO2) at week 3 were found in the IR group. In HPV-16 negative patients, significantly higher levels in tumor blood flow index and reduced scattering coefficient (μs′) at week 3 were observed in the IR group. These hemodynamic parameters exhibited significantly high accuracy for early prediction of clinical outcomes, within the first three weeks of therapy, with the areas under the receiver operating characteristic curves (AUCs) ranging from 0.83 to 0.96.

Lawrence K. Development of a combined broadband near-infrared and diffusion correlation system for monitoring cerebral blood flow and oxidative metabolism in preterm infants

Neonatal neuromonitoring is a major clinical focus of near-infrared spectroscopy (NIRS) and there is an increasing interest in measuring cerebral blood flow (CBF) and oxidative metabolism (CMRO2) in addition to the classic tissue oxygenation saturation (StO2). The purpose of this study was to assess the ability of broadband NIRS combined with diffusion correlation spectroscopy (DCS) to measured changes in StO2, CBF and CMRO2 in preterm infants undergoing pharmaceutical treatment of patent ductus arteriosus. CBF was measured by both DCS and contrast-enhanced NIRS for comparison. No significant difference in the treatment-induced CBF decrease was found between DCS (27.9 ± 2.2%) and NIRS (26.5 ± 4.3%). A reduction in StO2 (70.5 ± 2.4% to 63.7 ± 2.9%) was measured by broadband NIRS, reflecting the increase in oxygen extraction required to maintain CMRO2. This study demonstrates the applicability of broadband NIRS combined with DCS for neuromonitoring in this patient population.

Noncontact diffuse correlation tomography of human breast tumor

Our first step to adapt our recently developed noncontact diffuse correlation tomography (ncDCT) system for three-dimensional (3-D) imaging of blood flow distribution in human breast tumors is reported. A commercial 3-D camera was used to obtain breast surface geometry, which was then converted to a solid volume mesh. An ncDCT probe scanned over a region of interest on the mesh surface and the measured boundary data were combined with a finite element framework for 3-D image reconstruction of blood flow distribution. This technique was tested in computer simulations and in vivo human breasts with low-grade carcinoma. Results from computer simulations suggest that relatively high accuracy can be achieved when the entire tumor is within the sensitive region of diffuse light. Image reconstruction with a priori knowledge of the tumor volume and location can significantly improve the accuracy in recovery of tumor blood flow contrasts. In vivo imaging results from two breast carcinomas show higher average blood flow contrasts (5.9- and 10.9-fold) in the tumor regions compared to the surrounding tissues, which are comparable with previous findings using diffuse correlation spectroscopy. The ncDCT system has the potential to image blood flow distributions in soft and vulnerable tissues without distorting tissue hemodynamics

Assessing the reliability of diffuse correlation spectroscopy models on noise-free analytical Monte Carlo data

It is shown that an analytical noise-free implementation of Monte Carlo simulations [Appl. Opt.54, 2400 (2015).10.1364/AO.54.002400APOPAI1559-128X] for diffuse correlation spectroscopy (DCS) may be successfully used to check the ability of a given DCS model to generate a reliable estimator of tissue blood flow. As an example, four different DCS models often found in the scientific literature are tested on a simulated tissue (semi-infinite geometry) with a Maxwell-Boltzmann probability distribution function for red blood cell speed. It is shown that the random model is the best model for the chosen speed distribution but that (1) some inaccuracies in the DCS model in taking into account red blood cell concentration and (2) some inaccuracies, probably due to a low-order approximation of the DCS model, are still observed. The method can be easily generalized for other speed/flow probability distribution functions of the red blood cells.

Multidistance diffuse correlation spectroscopy for simultaneous estimation of blood flow index and optical properties

Traditionally, diffuse correlation spectroscopy (DCS) measures microvascular blood flow by fitting a physical model to the measurement of the intensity autocorrelation function from a single source-detector pair. This analysis relies on the accurate knowledge of the optical properties, absorption, and reduced scattering coefficients of the medium. Therefore, DCS is often deployed together with diffuse optical spectroscopy. We present an algorithm that employs multidistance DCS (MD-DCS) for simultaneous measurement of bloodflow index, as well as an estimate of the optical properties of the tissue. The algorithm has been validated through noise-free and noise-added simulated data and phantom measurements. A longitudinal in vivo measurement ofa mouse tumor is also shown. MD-DCS is introduced as a stand-alone system for small source-detector separations (<2 cm) for noninvasive measurement of microvascular blood flow.

Sensitivity of near-infrared spectroscopy and diffuse correlation spectroscopy to brain hemodynamics: simulations and experimental findings during hypercapnia

Near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) are two diffuse optical technologies for brain imaging that are sensitive to changes in hemoglobin concentrations and blood flow, respectively. Measurements for both modalities are acquired on the scalp, and therefore hemodynamic processes in the extracerebral vasculature confound the interpretation of cortical hemodynamic signals. The sensitivity of NIRS to the brain versus the extracerebral tissue and the contrast-to-noise ratio (CNR) of NIRS to cerebral hemodynamic responses have been well characterized, but the same has not been evaluated for DCS. This is important to assess in order to understand their relative capabilities in measuring cerebral physiological changes. We present Monte Carlo simulations on a head model that demonstrate that the relative brain-to-scalp sensitivity is about three times higher for DCS (0.3 at 3 cm) than for NIRS (0.1 at 3 cm). However, because DCS has higher levels of noise due to photon-counting detection, the CNR is similar for both modalities in response to a physiologically realistic simulation of brain activation. Even so, we also observed higher CNR of the hemodynamic response during graded hypercapnia in adult subjects with DCS than with NIRS.

Using optical fibers with different modes to improve the signal-to-noise ratio of diffuse correlation spectroscopy flow-oximeter measurements

The dual-wavelength diffuse correlation spectroscopy (DCS) flow-oximeter is an emerging technique enabling simultaneous measurements of blood flow and blood oxygenation changes in deep tissues. High signal-to-noise ratio (SNR) is crucial when applying DCS technologies in the study of human tissues where the detected signals are usually very weak. In this study, single-mode, few-mode, and multimode fibers are compared to explore the possibility of improving the SNR of DCS flow-oximeter measurements. Experiments on liquid phantom solutions and in vivo muscle tissues show only slight improvements in flow measurements when using the few-mode fiber compared with using the single-mode fiber. However, light intensities detected by the few-mode and multimode fibers are increased, leading to significant SNR improvements in detections of phantom optical property and tissue blood oxygenation. The outcomes from this study provide useful guidance for the selection of optical fibers to improve DCS flow-oximeter measurements.

Diffuse Correlation Spectroscopy (DCS) for Assessment of Tissue Blood Flow in Skeletal Muscle: Recent Progress

Near-infrared diffuse correlation spectroscopy (DCS) is an emerging technology for monitoring blood flow in various tissues. This article reviews the recent progress of DCS for the assessment of skeletal muscle blood flow, including the developments in technology allowing use during dynamic exercise and muscular electrical stimulation, the utilization for diagnosis of muscle vascular diseases, and the applications for evaluating treatment effects. The limitations of current DCS studies and future perspective are finally discussed.