In situ brain tumor detection using a Raman spectroscopy system-results of a multicenter study.

Safe and effective brain tumor surgery aims to remove tumor tissue, not non-tumoral brain. This is a challenge since tumor cells are often not visually distinguishable from peritumoral brain during surgery. To address this, we conducted a multicenter study testing whether the Sentry System could distinguish the three most common types of brain tumors from brain tissue in a label-free manner. The Sentry System is a new real time, in situ brain tumor detection device that merges Raman spectroscopy with machine learning tissue classifiers. Nine hundred and seventy-six in situ spectroscopy measurements and colocalized tissue specimens were acquired from 67 patients undergoing surgery for glioblastoma, brain metastases, or meningioma to assess tumor classification. The device achieved diagnostic accuracies of 91% for glioblastoma, 97% for brain metastases, and 96% for meningiomas. These data show that the Sentry System discriminated tumor containing tissue from non-tumoral brain in real time and prior to resection.

Experimental validation of a spectroscopic Monte Carlo light transport simulation technique and Raman scattering depth sensing analysis in biological tissue.

SIGNIFICANCE: Raman spectroscopy (RS) applied to surgical guidance is attracting attention among scientists in biomedical optics. Offering a computational platform for studying depth-resolved RS and probing molecular specificity of different tissue layers is of crucial importance to increase the precision of these techniques and facilitate their clinical adoption. AIM: The aim of this work was to present a rigorous analysis of inelastic scattering depth sampling and elucidate the relationship between sensing depth of the Raman effect and optical properties of the tissue under interrogation. APPROACH: A new Monte Carlo (MC) package was developed to simulate absorption, fluorescence, elastic, and inelastic scattering of light in tissue. The validity of the MC algorithm was demonstrated by comparison with experimental Raman spectra in phantoms of known optical properties using nylon and polydimethylsiloxane as Raman-active compounds. A series of MC simulations were performed to study the effects of optical properties on Raman sensing depth for an imaging geometry consistent with single-point detection using a handheld fiber optics probe system. RESULTS: The MC code was used to estimate the Raman sensing depth of a handheld fiber optics system. For absorption and reduced scattering coefficients of 0.001 and 1 mm - 1, the sensing depth varied from 105 to 225 µm for a range of Raman probabilities from 10 - 6 to 10 - 3. Further, for a realistic Raman probability of 10 - 6, the sensing depth ranged between 10 and 600 µm for the range of absorption coefficients 0.001 to 1.4 mm - 1 and reduced scattering coefficients of 0.5 to 30 mm - 1. CONCLUSIONS: A spectroscopic MC light transport simulation platform was developed and validated against experimental measurements in tissue phantoms and used to predict depth sensing in tissue. It is hoped that the current package and reported results provide the research community with an effective simulating tool to improve the development of clinical applications of RS.

Dynamic brain-body coupling of breath-by-breath O2-CO2 exchange ratio with resting state cerebral hemodynamic fluctuations.

BACKGROUND: The origin of low frequency cerebral hemodynamic fluctuations (CHF) in the resting state remains unknown. Breath-by breath O2-CO2 exchange ratio (bER) has been reported to correlate with the cerebrovascular response to brief breath hold challenge at the frequency range of 0.008-0.03Hz in healthy adults. bER is defined as the ratio of the change in the partial pressure of oxygen (ΔPO2) to that of carbon dioxide (ΔPCO2) between end inspiration and end expiration. In this study, we aimed to investigate the contribution of respiratory gas exchange (RGE) metrics (bER, ΔPO2 and ΔPCO2) to low frequency CHF during spontaneous breathing. METHODS: Twenty-two healthy adults were included. We used transcranial Doppler sonography to evaluate CHF by measuring the changes in cerebral blood flow velocity (ΔCBFv) in bilateral middle cerebral arteries. The regional CHF were mapped with blood oxygenation level dependent (ΔBOLD) signal changes using functional magnetic resonance imaging. Temporal features and frequency characteristics of RGE metrics during spontaneous breathing were examined, and the simultaneous measurements of RGE metrics and CHF (ΔCBFv and ΔBOLD) were studied for their correlation. RESULTS: We found that the time courses of ΔPO2 and ΔPCO2 were interdependent but not redundant. The oscillations of RGE metrics were coherent with resting state CHF at the frequency range of 0.008-0.03Hz. Both bER and ΔPO2 were superior to ΔPCO2 in association with CHF while CHF could correlate more strongly with bER than with ΔPO2 in some brain regions. Brain regions with the strongest coupling between bER and ΔBOLD overlapped with many areas of default mode network including precuneus and posterior cingulate. CONCLUSION: Although the physiological mechanisms underlying the strong correlation between bER and CHF are unclear, our findings suggest the contribution of bER to low frequency resting state CHF, providing a novel insight of brain-body interaction via CHF and oscillations of RGE metrics.

Cerebrovascular reactivity assessment with O2-CO2 exchange ratio under brief breath hold challenge.

BACKGROUND: Hypercapnia during breath holding is believed to be the dominant driver behind the modulation of cerebral blood flow (CBF). However, increasing evidence show that mild hypoxia and mild hypercapnia in breath hold (BH) could work synergistically to enhance CBF response. We hypothesized that breath-by-breath O2-CO2 exchange ratio (bER), defined as the ratio of the change in partial pressure of oxygen (ΔPO2) to that of carbon dioxide (ΔPCO2) between end inspiration and end expiration, would be able to better correlate with the global and regional cerebral hemodynamic responses (CHR) to BH challenge. We aimed to investigate whether bER is a more useful index than end-tidal PCO2 to characterize cerebrovascular reactivity (CVR) under BH challenge. METHODS: We used transcranial Doppler ultrasound (TCD) to evaluate CHR under BH challenge by measuring cerebral blood flow velocity (CBFv) in the middle cerebral arteries. Regional changes in CHR to BH and exogenous CO2 challenges were mapped with blood oxygenation level dependent (BOLD) signal changes using functional magnetic resonance imaging (fMRI). We correlated respiratory gas exchange (RGE) metrics (bER, ΔPO2, ΔPCO2, end-tidal PCO2 and PO2, and time of breaths) with CHR (CBFv and BOLD) to BH challenge. Temporal features and frequency characteristics of RGE metrics and their coherence with CHR were examined. RESULTS: CHR to brief BH epochs and free breathing were coupled with both ΔPO2 and ΔPCO2. We found that bER was superior to either ΔPO2 or ΔPCO2 alone in coupling with the changes of CBFv and BOLD signals under breath hold challenge. The regional CVR results derived by regressing BOLD signal changes on bER under BH challenge resembled those derived by regressing BOLD signal changes on end-tidal PCO2 under exogenous CO2 challenge. CONCLUSION: Our findings provide a novel insight on the potential of using bER to better quantify CVR changes under BH challenge.

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

[This corrects the article DOI: 10.1117/1.NPh.5.4.045005.].

Non-invasive methods for measuring vascular changes in neurovascular headaches.

Vascular changes during spontaneous headache attacks have been studied over the last 30 years. The interest in cerebral vessels in headache research was initially due to the hypothesis of cerebral vessels as the pain source. Here, we review the knowledge gained by measuring the cerebral vasculature during spontaneous primary headache attacks with the use of single photon emission tomography (SPECT), positron emission tomography (PET), magnetic resonance imaging (MRA) and transcranial Doppler (TCD). Furthermore, the use of near-infrared spectroscopy in headache research is reviewed. Existing TCD studies of migraine and other headache disorders do not provide solid evidence for cerebral blood flow velocity changes during spontaneous attacks of migraine headache. SPECT studies have clearly shown cortical vascular changes following migraine aura and the differences between migraine with aura compared to migraine without aura. PET studies have shown focal activation in brain structures related to headache, but whether the changes are specific to different primary headaches have yet to be demonstrated. MR angiography has shown precise changes in large cerebral vessels during spontaneous migraine without aura attacks. Future development in more precise imaging methods may further elucidate the pathophysiological mechanisms in primary headaches.

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.

Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis.

Diffuse optical tomography (DOT) is emerging as a noninvasive functional imaging method for breast cancer diagnosis and neoadjuvant chemotherapy monitoring. In particular, the multimodal approach of combining DOT with x-ray digital breast tomosynthesis (DBT) is especially synergistic as DBT prior information can be used to enhance the DOT reconstruction. DOT, in turn, provides a functional information overlay onto the mammographic images, increasing sensitivity and specificity to cancer pathology. We describe a dynamic DOT apparatus designed for tight integration with commercial DBT scanners and providing a fast (up to 1 Hz) image acquisition rate to enable tracking hemodynamic changes induced by the mammographic breast compression. The system integrates 96 continuous-wave and 24 frequency-domain source locations as well as 32 continuous wave and 20 frequency-domain detection locations into low-profile plastic plates that can easily mate to the DBT compression paddle and x-ray detector cover, respectively. We demonstrate system performance using static and dynamic tissue-like phantoms as well as in vivo images acquired from the pool of patients recalled for breast biopsies at the Massachusetts General Hospital Breast Imaging Division.

Functional Near Infrared Spectroscopy: Enabling Routine Functional Brain Imaging.

Functional Near-Infrared Spectroscopy (fNIRS) maps human brain function by measuring and imaging local changes in hemoglobin concentrations in the brain that arise from the modulation of cerebral blood flow and oxygen metabolism by neural activity. Since its advent over 20 years ago, researchers have exploited and continuously advanced the ability of near infrared light to penetrate through the scalp and skull in order to non-invasively monitor changes in cerebral hemoglobin concentrations that reflect brain activity. We review recent advances in signal processing and hardware that significantly improve the capabilities of fNIRS by reducing the impact of confounding signals to improve statistical robustness of the brain signals and by enhancing the density, spatial coverage, and wearability of measuring devices respectively. We then summarize the application areas that are experiencing rapid growth as fNIRS begins to enable routine functional brain imaging.

Time-domain diffuse correlation spectroscopy.

Physiological monitoring of oxygen delivery to the brain has great significance for improving the management of patients at risk for brain injury. Diffuse correlation spectroscopy (DCS) is a rapidly growing optical technology able to non-invasively assess the blood flow index (BFi) at the bedside. The current limitations of DCS are the contamination introduced by extracerebral tissue and the need to know the tissue's optical properties to correctly quantify the BFi. To overcome these limitations, we have developed a new technology for time-resolved diffuse correlation spectroscopy. By operating DCS in the time domain (TD-DCS), we are able to simultaneously acquire the temporal point-spread function to quantify tissue optical properties and the autocorrelation function to quantify the BFi. More importantly, by applying time-gated strategies to the DCS autocorrelation functions, we are able to differentiate between short and long photon paths through the tissue and determine the BFi for different depths. Here, we present the novel device and we report the first experiments in tissue-like phantoms and in rodents. The TD-DCS method opens many possibilities for improved non-invasive monitoring of oxygen delivery in humans.

Mayer waves reduce the accuracy of estimated hemodynamic response functions in functional near-infrared spectroscopy.

Analysis of cerebral hemodynamics reveals a wide spectrum of oscillations ranging from 0.0095 to 2 Hz. While most of these oscillations can be filtered out during analysis of functional near-infrared spectroscopy (fNIRS) signals when estimating stimulus evoked hemodynamic responses, oscillations around 0.1 Hz are an exception. This is due to the fact that they share a common spectral range with typical stimulus evoked hemodynamic responses from the brain. Here we investigate the effect of hemodynamic oscillations around 0.1 Hz on the estimation of hemodynamic response functions from fNIRS data. Our results show that for an expected response of ~1 µM in oxygenated hemoglobin concentration (HbO), Mayer wave oscillations with an amplitude > ~1 µM at 0.1 Hz reduce the accuracy of the estimated response as quantified by a 3 fold increase in the mean squared error and decrease in correlation (R(2) below 0.78) when compared to the true HRF. These results indicate that the amplitude of oscillations at 0.1 Hz can serve as an objective metric of the expected HRF estimation accuracy. In addition, we investigated the effect of short separation regression on the recovered HRF, and found that this improves the recovered HRF when large amplitude 0.1 Hz oscillations are present in fNIRS data. We suspect that the development of other filtering strategies may provide even further improvement.

Establishing the diffuse correlation spectroscopy signal relationship with blood flow.

Diffuse correlation spectroscopy (DCS) measurements of blood flow rely on the sensitivity of the temporal autocorrelation function of diffusively scattered light to red blood cell (RBC) mean square displacement (MSD). For RBCs flowing with convective velocity [Formula: see text], the autocorrelation is expected to decay exponentially with [Formula: see text], where [Formula: see text] is the delay time. RBCs also experience shear-induced diffusion with a diffusion coefficient [Formula: see text] and an MSD of [Formula: see text]. Surprisingly, experimental data primarily reflect diffusive behavior. To provide quantitative estimates of the relative contributions of convective and diffusive movements, we performed Monte Carlo simulations of light scattering through tissue of varying vessel densities. We assumed laminar vessel flow profiles and accounted for shear-induced diffusion effects. In agreement with experimental data, we found that diffusive motion dominates the correlation decay for typical DCS measurement parameters. Furthermore, our model offers a quantitative relationship between the RBC diffusion coefficient and absolute tissue blood flow. We thus offer, for the first time, theoretical support for the empirically accepted ability of the DCS blood flow index ([Formula: see text]) to quantify tissue perfusion. We find [Formula: see text] to be linearly proportional to blood flow, but with a proportionality modulated by the hemoglobin concentration and the average blood vessel diameter.

Effect of motion artifacts and their correction on near-infrared spectroscopy oscillation data: a study in healthy subjects and stroke patients.

Functional near-infrared spectroscopy is prone to contamination by motion artifacts (MAs). Motion correction algorithms have previously been proposed and their respective performance compared for evoked rain activation studies. We study instead the effect of MAs on "oscillation" data which is at the basis of functional connectivity and autoregulation studies. We use as our metric of interest the interhemispheric correlation (IHC), the correlation coefficient between symmetrical time series of oxyhemoglobin oscillations. We show that increased motion content results in a decreased IHC. Using a set of motion-free data on which we add real MAs, we find that the best motion correction approach consists of discarding the segments of MAs following a careful approach to minimize the contamination due to band-pass filtering of data from "bad" segments spreading into adjacent "good" segments. Finally, we compare the IHC in a stroke group and in a healthy group that we artificially contaminated with the MA content of the stroke group, in order to avoid the confounding effect of increased motion incidence in the stroke patients. After motion correction, the IHC remains lower in the stroke group in the frequency band around 0.1 and 0.04 Hz, suggesting a physiological origin for the difference. We emphasize the importance of considering MAs as a confounding factor in oscillation-based functional near-infrared spectroscopy studies.

Short separation regression improves statistical significance and better localizes the hemodynamic response obtained by near-infrared spectroscopy for tasks with differing autonomic responses.

Autonomic nervous system response is known to be highly task-dependent. The sensitivity of near-infrared spectroscopy (NIRS) measurements to superficial layers, particularly to the scalp, makes it highly susceptible to systemic physiological changes. Thus, one critical step in NIRS data processing is to remove the contribution of superficial layers to the NIRS signal and to obtain the actual brain response. This can be achieved using short separation channels that are sensitive only to the hemodynamics in the scalp. We investigated the contribution of hemodynamic fluctuations due to autonomous nervous system activation during various tasks. Our results provide clear demonstrations of the critical role of using short separation channels in NIRS measurements to disentangle differing autonomic responses from the brain activation signal of interest.

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.

TARGETED PRINCIPLE COMPONENT ANALYSIS: A NEW MOTION ARTIFACT CORRECTION APPROACH FOR NEAR-INFRARED SPECTROSCOPY.

As near-infrared spectroscopy (NIRS) broadens its application area to different age and disease groups, motion artifacts in the NIRS signal due to subject movement is becoming an important challenge. Motion artifacts generally produce signal fluctuations that are larger than physiological NIRS signals, thus it is crucial to correct for them before obtaining an estimate of stimulus evoked hemodynamic responses. There are various methods for correction such as principle component analysis (PCA), wavelet-based filtering and spline interpolation. Here, we introduce a new approach to motion artifact correction, targeted principle component analysis (tPCA), which incorporates a PCA filter only on the segments of data identified as motion artifacts. It is expected that this will overcome the issues of filtering desired signals that plagues standard PCA filtering of entire data sets. We compared the new approach with the most effective motion artifact correction algorithms on a set of data acquired simultaneously with a collodion-fixed probe (low motion artifact content) and a standard Velcro probe (high motion artifact content). Our results show that tPCA gives statistically better results in recovering hemodynamic response function (HRF) as compared to wavelet-based filtering and spline interpolation for the Velcro probe. It results in a significant reduction in mean-squared error (MSE) and significant enhancement in Pearson's correlation coefficient to the true HRF. The collodion-fixed fiber probe with no motion correction performed better than the Velcro probe corrected for motion artifacts in terms of MSE and Pearson's correlation coefficient. Thus, if the experimental study permits, the use of a collodion-fixed fiber probe may be desirable. If the use of a collodion-fixed probe is not feasible, then we suggest the use of tPCA in the processing of motion artifact contaminated data.

Validation of the hypercapnic calibrated fMRI method using DOT-fMRI fusion imaging.

Calibrated functional magnetic resonance imaging (fMRI) is a widely used method to investigate brain function in terms of physiological quantities such as the cerebral metabolic rate of oxygen (CMRO2). The first and one of the most common methods of fMRI calibration is hypercapnic calibration. This is achieved via simultaneous measures of the blood-oxygenation-level dependent (BOLD) and the arterial spin labeling (ASL) signals during a functional task that evokes regional changes in CMRO2. A subsequent acquisition is then required during which the subject inhales carbon dioxide for short periods of time. A calibration constant, typically labeled M, is then estimated from the hypercapnic data and is subsequently used together with the BOLD-ASL recordings to compute evoked changes in CMRO2 during the functional task. The computation of M assumes a constant CMRO2 during the CO2 inhalation, an assumption that has been questioned since the origin of calibrated fMRI. In this study we used diffuse optical tomography (DOT) together with BOLD and ASL--an alternative calibration method that does not require any gas manipulation and therefore no constant CMRO2 assumption--to cross-validate the estimation of M obtained from a traditional hypercapnic calibration. We found a high correlation between the M values (R=0.87, p<0.01) estimated using these two approaches. The findings serve to validate the hypercapnic fMRI calibration technique and suggest that the inter-subject variability routinely obtained for M is reproducible with an alternative method and might therefore reflect inter-subject physiological variability.

Comparison of a layered slab and an atlas head model for Monte Carlo fitting of time-domain near-infrared spectroscopy data of the adult head.

Near-infrared spectroscopy (NIRS) estimations of the adult brain baseline optical properties based on a homogeneous model of the head are known to introduce significant contamination from extracerebral layers. More complex models have been proposed and occasionally applied to in vivo data, but their performances have never been characterized on realistic head structures. Here we implement a flexible fitting routine of time-domain NIRS data using graphics processing unit based Monte Carlo simulations. We compare the results for two different geometries: a two-layer slab with variable thickness of the first layer and a template atlas head registered to the subject's head surface. We characterize the performance of the Monte Carlo approaches for fitting the optical properties from simulated time-resolved data of the adult head. We show that both geometries provide better results than the commonly used homogeneous model, and we quantify the improvement in terms of accuracy, linearity, and cross-talk from extracerebral layers.

Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data.

Motion artifacts are a significant source of noise in many functional near-infrared spectroscopy (fNIRS) experiments. Despite this, there is no well-established method for their removal. Instead, functional trials of fNIRS data containing a motion artifact are often rejected completely. However, in most experimental circumstances the number of trials is limited, and multiple motion artifacts are common, particularly in challenging populations. Many methods have been proposed recently to correct for motion artifacts, including principle component analysis, spline interpolation, Kalman filtering, wavelet filtering and correlation-based signal improvement. The performance of different techniques has been often compared in simulations, but only rarely has it been assessed on real functional data. Here, we compare the performance of these motion correction techniques on real functional data acquired during a cognitive task, which required the participant to speak aloud, leading to a low-frequency, low-amplitude motion artifact that is correlated with the hemodynamic response. To compare the efficacy of these methods, objective metrics related to the physiology of the hemodynamic response have been derived. Our results show that it is always better to correct for motion artifacts than reject trials, and that wavelet filtering is the most effective approach to correcting this type of artifact, reducing the area under the curve where the artifact is present in 93% of the cases. Our results therefore support previous studies that have shown wavelet filtering to be the most promising and powerful technique for the correction of motion artifacts in fNIRS data. The analyses performed here can serve as a guide for others to objectively test the impact of different motion correction algorithms and therefore select the most appropriate for the analysis of their own fNIRS experiment.

Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers.

As the applications of near-infrared spectroscopy (NIRS) continue to broaden and long-term clinical monitoring becomes more common, minimizing signal artifacts due to patient movement becomes more pressing. This is particularly true in applications where clinically and physiologically interesting events are intrinsically linked to patient movement, as is the case in the study of epileptic seizures. In this study, we apply an approach common in the application of EEG electrodes to the application of specialized NIRS optical fibers. The method provides improved optode-scalp coupling through the use of miniaturized optical fiber tips fixed to the scalp using collodion, a clinical adhesive. We investigate and quantify the performance of this new method in minimizing motion artifacts in healthy subjects, and apply the technique to allow continuous NIRS monitoring throughout epileptic seizures in two epileptic in-patients. Using collodion-fixed fibers reduces the percent signal change of motion artifacts by 90% and increases the SNR by 6 and 3 fold at 690 and 830 nm wavelengths respectively when compared to a standard Velcro-based array of optical fibers. The SNR has also increased by 2 fold during rest conditions without motion with the new probe design because of better light coupling between the fiber and scalp. The change in both HbO and HbR during motion artifacts is found to be statistically lower for the collodion-fixed fiber probe. The collodion-fixed optical fiber approach has also allowed us to obtain good quality NIRS recording of three epileptic seizures in two patients despite excessive motion in each case.