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.
Corrected disclosures for the article “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 (CBFi) 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 CBFi 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.
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.
KEYWORDS: Near infrared spectroscopy, Monte Carlo methods, Brain, Oxygen, Spectroscopy, Mode conditioning cables, Tissues, Head, Magnetic resonance imaging, Thermal modeling
Diffuse correlation spectroscopy (DCS) is being employed alongside near-infrared spectroscopy (NIRS) measurements to track the cerebral oxygen metabolic rate (CMRO2). However, both techniques employ diffusely reflected light that has traveled mostly through extracerebral tissues. Recent studies indicate that depth sensitivity profiles are different for NIRS vs DCS measurements, with DCS appearing to be more sensitive to the brain than NIRS methods for a given source-detector separation. This mismatch can lead to erroneous conclusions with respect to the amount and perhaps even the direction of change in CMRO2. Recently, our group and others have demonstrated the use of Monte Carlo (MC) based multi-layer, multi-distance fitting, which offers increased accuracy for complex tissue structures such as the adult brain.
In this paper we employ a Monte Carlo light transport model based on a realistic head geometry that can be derived from MRI scans (if available) or approximated from head shape measurements. We consider DCS and CW-NIRS measurements taken at two or more distances and analyze simulated data generated using a fully segmented adult brain MRI scan. Through simulations, we explore the improvements offered by our method vs. processing the same measurements with a semi-infinite diffusion model and estimate the impact of errors in geometry and optical properties on relative blood flow and CMRO2 changes.
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 vRBC, the autocorrelation is expected to decay exponentially with (vRBCτ)2, where τ is the delay time. RBCs also experience shear-induced diffusion with a diffusion coefficient Dshear and an MSD of 6Dshearτ. 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 (BFi) to quantify tissue perfusion. We find BFi to be linearly proportional to blood flow, but with a proportionality modulated by the hemoglobin concentration and the average blood vessel diameter.
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.
KEYWORDS: Near infrared spectroscopy, Contamination, Wavelets, Principal component analysis, Linear filtering, Bandpass filters, Data modeling, Motion analysis, Signal to noise ratio, Functional magnetic resonance imaging
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 brain 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.
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.
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.
We have developed the second generation of our time-domain near-infrared spectroscopy (TD-NIRS) system for baseline and functional brain imaging. The instrument uses a pulsed broadband supercontinuum laser emitting a large spectrum between 650 and 1700 nm, and a gated detection based on an intensified CCD camera. The source laser beam is split into two arms, below and above 776 nm. In each arm, a fast motorized filter wheel enables selection of a bandpass filter at the required wavelength. Each filtered laser beam is then launched into one array of source fibers. The multiplexing through the array of fibers is implemented through a very compact home-made design consisting of two galvanometer mirrors followed by an achromatic doublet. Source fibers are then recombined one-by-one from both arms into the source optodes to be positioned on the head. The detection fibers are all imaged in parallel through a relay lens on an intensified CCD camera. By using detection fibers of different lengths, we introduce optical delays that enable simultaneous recording in different delay windows of the temporal point spread functions. We present the instrumentation and show its preliminary functional imaging capabilities. We also introduce a new probe where we use different fiber lengths on the source and the detector sides in order to record simultaneously both wavelengths from one location through different sets of fibers.
Time-domain near-infrared spectroscopy (TD-NIRS) offers the ability to measure the absolute baseline optical properties of a tissue. Specifically, for brain imaging, the robust assessment of cerebral blood volume and oxygenation based on measurement of cerebral hemoglobin concentrations is essential for reliable cross-sectional and longitudinal studies. In adult heads, these baseline measurements are complicated by the presence of thick extra-cerebral tissue (scalp, skull, CSF). A simple semi-infinite homogeneous model of the head has proven to have limited use because of the large errors it introduces in the recovered brain absorption. Analytical solutions for layered media have shown improved performance on Monte-Carlo simulated data and layered phantom experiments, but their validity on real adult head data has never been demonstrated. With the advance of fast Monte Carlo approaches based on GPU computation, numerical methods to solve the radiative transfer equation become viable alternatives to analytical solutions of the diffusion equation. Monte Carlo approaches provide the additional advantage to be adaptable to any geometry, in particular more realistic head models. The goals of the present study were twofold: (1) to implement a fast and flexible Monte Carlo-based fitting routine to retrieve the brain optical properties; (2) to characterize the performances of this fitting method on realistic adult head data. We generated time-resolved data at various locations over the head, and fitted them with different models of light propagation: the homogeneous analytical model, and Monte Carlo simulations for three head models: a two-layer slab, the true subject’s anatomy, and that of a generic atlas head. We found that the homogeneous model introduced a median 20 to 25% error on the recovered brain absorption, with large variations over the range of true optical properties. The two-layer slab model only improved moderately the results over the homogeneous one. On the other hand, using a generic atlas head registered to the subject’s head surface decreased the error by a factor of 2. When the information is available, using the true subject anatomy offers the best performance.
KEYWORDS: Near infrared spectroscopy, Veins, Monte Carlo methods, Brain, In vivo imaging, Tissue optics, Functional magnetic resonance imaging, Tissues, Oxygen, Data modeling
Near-Infrared Spectroscopy (NIRS) measures the functional hemodynamic response occuring at the surface of
the cortex. Large pial veins are located above the surface of the cerebral cortex. Following activation, these
veins exhibit oxygenation changes but their volume likely stays constant. The back-reflection geometry of the
NIRS measurement renders the signal very sensitive to these superficial pial veins. As such, the measured NIRS
signal contains contributions from both the cortical region as well as the pial vasculature. In this work, the
cortical contribution to the NIRS signal was investigated using (1) Monte Carlo simulations over a realistic
geometry constructed from anatomical and vascular MRI and (2) multimodal NIRS-BOLD recordings during
motor stimulation. A good agreement was found between the simulations and the modeling analysis of in vivo
measurements. Our results suggest that the cortical contribution to the deoxyhemoglobin signal change (ΔHbR)
is equal to 16-22% of the cortical contribution to the total hemoglobin signal change (ΔHbT). Similarly, the
cortical contribution of the oxyhemoglobin signal change (ΔHbO) is equal to 73-79% of the cortical contribution
to the ΔHbT signal. These results suggest that ΔHbT is far less sensitive to pial vein contamination and
therefore, it is likely that the ΔHbT signal provides better spatial specificity and should be used instead of
ΔHbO or ΔHbR to map cerebral activity with NIRS. While different stimuli will result in different pial vein
contributions, our finger tapping results do reveal the importance of considering the pial contribution.
NIRS is safe, non-invasive and offers the possibility to record local hemodynamic parameters at the bedside,
avoiding the transportation of neonates and critically ill patients. In this work, we evaluate the accuracy of the
frequency-domain multi-distance (FD-MD) method to retrieve brain optical properties from neonate to adult.
Realistic measurements are simulated using a 3D Monte Carlo modeling of light propagation. Height different
ages were investigated: a term newborn of 38 weeks gestational age, two infants of 6 and 12 months of age,
a toddler of 2 year (yr.) old, two children of 5 and 10 years of age, a teenager of 14 yr. old, and an adult.
Measurements are generated at multiple distances on the right parietal area of head models and fitted to a
homogeneous FD-MD model to estimate the brain optical properties. In the newborn, infants, toddler and 5 yr.
old child models, the error was dominated by the head curvature, while the superficial layer in the 10 yr. old
child, teenager and adult heads. The influence of the CSF is also evaluated. In this case, absorption coefficients
suffer from an additional error. In all cases, measurements at 5 mm provided worse estimation because of the
diffusion approximation.
We present in vivo measurements of baseline physiology from five subjects with a four-wavelength (690, 750, 800, and 850 nm) time-resolved optical system. The measurements were taken at four distances: 10, 15, 25, and 30 mm. All distances were fit simultaneously with a two-layered analytical model for the absorption and reduced scattering coefficient of both layers. The thickness of the first layer, comprising the skin, scalp, and cerebrospinal fluid, was obtained from anatomical magnetic resonance images. The fitting procedure was first tested with simulations before being applied to in vivo measurements and verified that this procedure permits accurate characterization of the hemoglobin concentrations in the extra- and intracerebral tissues. Baseline oxyhemoglobin, deoxyhemoglobin, and total hemoglobin concentrations and oxygen saturation were recovered from in vivo measurements and compared to the literature. We observed a noticeable intersubject variability of the hemoglobin concentrations, but constant values for the cerebral hemoglobin oxygen saturation.
In the course of our experiments imaging the compressed breast in conjunction with digital tomosynthesis,
we have noted that significant changes in tissue optical properties, on the order of 5%, occur during our
imaging protocol. These changes seem to consistent with changes both in total Hemoglobin concentration
as well as in oxygen saturation, as was the case for our standalone breast compression study, which made
use of reflectance measurements. Simulation experiments show the importance of taking into account the
temporal dynamics in the image reconstruction, and demonstrate the possibility of imaging the spatio-temporal
dynamics of oxygen saturation and total Hemoglobin in the breast. In the image reconstruction,
we make use of spatio-temporal basis functions, specifically a voxel basis for spatial imaging, and a cubic
spline basis in time, and we reconstruct the spatio-temporal images using the entire data set simultaneously,
making use of both absolute and relative measurements in the cost function. We have modified the sequence of sources used in our imaging acquisition protocol to improve our temporal resolution, and preliminary results are shown for normal subjects.
Combining 2D X-ray mammography or 3D tomosynthesis with diffuse optical tomography for breast imaging
is advantageous in facilitating clinical diagnosis by fusing the structural X-ray images with functional optical
images. In this study, we imaged 65 patients with a combined tomosynthesis/diffuse optical breast imaging
system developed at Massachusetts General Hospital. The bulk optical properties and patient demographics were
summarized in this paper. The averaged total-hemoglobin for 60 healthy breasts is 21 &mgr;M which is comparable
with literature values given the applied mammographic compression in our experiments. The averaged oxygen
saturation is 76%. The comparison of contra-lateral breast measurements also demonstrated correlations in
total hemoglobin and oxygen saturation. Image reconstructions of the healthy breasts with moderate-sized fibroglandular
regions correctly recovered the chest-wall muscle, fibro-glandular tissue as well as the surrounding fatty
tissue. For dense breasts, the contrast between the chest-wall and the fibro-glandular region is small and the
most pronounced feature of the image is a low-absorption region in the center of the breast. We hypothesized
that this is caused by pressure induced blood-redistribution. Supportive evidence for this hypothesis had been
shown with mechanical simulations of breast compression.
We present a time-domain optical system for functional imaging of the adult head. We first describe the instrument, which is based on a Ti:Sapphire pulsed laser (wavelength 750–850 nm) and an intensified CCD camera enabling parallel detection of multiple fibers. We characterize the system in terms of sensitivity and signal-to-noise ratio, instrument response function, cross-talk, stability, and reproducibility. We then describe two applications of the instrument: the characterization of baseline optical properties of homogeneous scattering media, and functional brain imaging. For the second application, we developed a two-part probe consisting in two squares of 4×4 sources and 3×3 detectors. The laser source is time-multiplexed to define 4 states of 8 sources that can be turned on during the same camera frame while minimizing cross-talk. On the detection side, we use for each detector 7 fibers of different lengths creating an optical delay, and enabling simultaneous detection in 7 windows (by steps of 500 ps) for each detector. This multiple window detection allows depth sensitivity. The imaging probe was tested on dynamic phantoms and a preliminary result on an adult performing a motor task shows discrimination between superficial and cortical responses to the stimulus on both hemispheres.
KEYWORDS: Continuous wave operation, Signal to noise ratio, Sensors, Absorption, Hemodynamics, Tissues, Interference (communication), Signal detection, Brain activation, Systems modeling
Time domain (TD) diffuse optical measurement systems are being applied to neuroimaging, where they can detect hemodynamics changes associated with cerebral activity. We show that TD systems can provide better depth sensitivity than the more traditional continuous wave (CW) systems by gating late photons, which carry information about deep layers of the brain, and rejecting early light, which is sensitive to the superficial physiological signal clutter. We use an analytical model to estimate the contrast due to an activated region of the brain, the instrumental noise of the systems, and the background signal resulting from superficial physiological signal clutter. We study the contrast-to-noise ratio and the contrast-to-background ratio as a function of the activation depth and of the source-detector separation. We then present experimental results obtained with a time-gated instrument on the motor cortex during finger-tapping exercises. Both the model and the experimental results show a similar contrast-to-noise ratio for CW and TD, but that estimation of the contrast is experimentally limited by background fluctuations and that a better contrast-to-background ratio is obtained in the TD case. Finally, we use the time-gated measurements to resolve in depth the brain activation during the motor stimulus.
The principle of acousto-optic imaging is to combine coherent light and ultrasound in biological tissues to detect optical contrasts with a spatial resolution close to that of echography. Here we propose a model to describe the propagation of acoustically modulated light in a scattering medium. To do so, we derive a correlation diffusion equation from a correlation transport equation making a few approximations. We express solutions of this diffusion equation and compare them with Monte Carlo simulations in the case of uniform and localized insonifications.
Although tumors can show important contrast in their optical
properties at an early stage of development, they are difficult to
image optically due the diffusive nature of biological tissues. Such tumors can also be detected by "classical" ultrasound (US) imaging, but the acoustic constrast is often weak at early stages. Acousto-optical (AO) imaging combines light and ultrasound : light carries the desired information and ultrasound provides the spatial resolution. Based on a previous work made by the group of L.V. Wang, we present AO images obtained with chirped US. This modulation of the US frequency allows to encode a spatial region of the medium in the frequency spectrum of the AO signal. We can then obtain
the optical contrast along the US path with improved resolution. The
technique was apply to the imaging of buried objects in phantoms and
to the vizualization of the "virtual source".
Acousto-optic imaging in strongly light-scattering tissues seeks to reveal optical contrasts in these turbid media. Nevertheless, this technique happens to be also sensitive to their acoustic contrasts. We have built a new setup combining a dedicated echograph and an acousto-optic imager in a single apparatus. Thanks to this setup, we have studied ultrasound absorbent and light absorbent features embedded in several centimeter thick biological tissues, and we have compared for the first time the acoustic and acousto-optic signals recorded in the same configuration. We show that even though optical contrast is the ultimate goal of this technique, preliminary acoustic investigation of the tissue is necessary to interpret correctly acousto-optical signals.
Acousto-optic imaging consists in tagging multi-scattered photon paths with a focused ultrasonic beam. This technique should give optical information on hidden structures in several centimeter thick scattering media, with a millimetric resolution. We have coupled our previous acousto-optic imaging setup with a suitably designed echograph. Thanks to a single 3 MHz multi-ring emitter, working either in pulsed or c.w. mode, we can get acoustic as well as acousto-optic responses of structures in biological tissues.
We have demonstrated the feasibility of tagging the photons trajectories with a focused ultrasonic field, to reveal optical contrast in biological tissues. 3D images have been obtained on real ex-vivo structures of animal as well as human tissues, through a thickness ranging from 2 cm to 4.5 cm. We are developing the coupling of this acousto-optical imaging with a traditional echograph.
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