As an application of near-infrared spectroscopy, pulse oximetry is widely used to non-invasively measure the arterial oxygen saturation (SpO2). To improve the maternal and fetal outcome during delivery, transabdominal fetal pulse oximetry can be used to measure the fetal SpO2. The layered tissue structure above the fetus, however, is complex and thick. In order to understand the feasibility of transabdominal pulse oximetry, we simulated light propagation through the maternal abdomen. For realistic geometry, we segmented a magnetic resonance imaging (MRI) scan of a pregnant women to create a 3D anatomical model, from which a 3D tetrahedron mesh was generated. Using this mesh, we then simulated photon propagation for 5 wavelengths and a grid of 70 sources and detectors (35 each) on the maternal abdomen above the fetal head with NIRFAST. Finally, depth sensitives were examined with Jacobian (J) and flat field. For a fetal head at ~4 cm depth, we found the normalized J at this depth is ~0.1-1% for source-detector distances within 9-10 cm. We also observed that at the same depth, the normalized flat field sensitivity is ~5-10%, which is 1-2 orders of magnitude higher than the normalized J. These results indicate that enough light can reach the fetus when considering ~9 cm source detector distances.
SignificanceUsing functional near-infrared spectroscopy (fNIRS) in bottlenose dolphins (Tursiops truncatus) could help to understand how echolocating animals perceive their environment and how they focus on specific auditory objects, such as fish, in noisy marine settings.AimTo test the feasibility of near-infrared spectroscopy (NIRS) in medium-sized marine mammals, such as dolphins, we modeled the light propagation with computational tools to determine the wavelengths, optode locations, and separation distances that maximize sensitivity to brain tissue.ApproachUsing frequency-domain NIRS, we measured the absorption and reduced scattering coefficient of dolphin sculp. We assigned muscle, bone, and brain optical properties from the literature and modeled light propagation in a spatially accurate and biologically relevant model of a dolphin head, using finite-element modeling. We assessed tissue sensitivities for a range of wavelengths (600 to 1700 nm), source–detector distances (50 to 120 mm), and animal sizes (juvenile model 25% smaller than adult).ResultsWe found that the wavelengths most suitable for imaging the brain fell into two ranges: 700 to 900 nm and 1100 to 1150 nm. The optimal location for brain sensing positioned the center point between source and detector 30 to 50 mm caudal of the blowhole and at an angle 45 deg to 90 deg lateral off the midsagittal plane. Brain tissue sensitivity comparable to human measurements appears achievable only for smaller animals, such as juvenile bottlenose dolphins or smaller species of cetaceans, such as porpoises, or with source–detector separations ≫100 mm in adult dolphins.ConclusionsBrain measurements in juvenile or subadult dolphins, or smaller dolphin species, may be possible using specialized fNIRS devices that support optode separations of >100 mm. We speculate that many measurement repetitions will be required to overcome hemodynamic signals originating predominantly from the muscle layer above the skull. NIRS measurements of muscle tissue are feasible today with source–detector separations of 50 mm, or even less.
Intracranial pressure (ICP) measurements help monitor patient status following cerebral injury, and currently require implantation of an invasive pressure probe. The potential complications associated with this implantation have restricted the application of ICP measurements in less severe conditions. We propose a non-invasive alternative that derives features from the cardiac waveforms present in near-infrared spectroscopy (NIRS) measurements and inputs these features into a decision tree regressor to estimate ICP. We evaluated this method in nine subjects already fitted with invasive ICP sensors. The non-invasive nature of NIRS instrumentation eases the clinical adoption of this ICP estimation approach.
Cerebral autoregulation (CA) is a mechanism to maintain cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP), through active vasoconstriction and vasodilation of arterioles in the brain. Dynamic CA is believed to act as a high-pass filter such that only low frequency changes in pressure are counteracted by an active vasculature response. With high frequency oscillations in pressure, such as those that occur at the heart rate (HR), the effects of dynamic CA are absent and changes in CPP are passively transmitted to CBF based on the cerebrovascular resistance (CVR) and compliance (CVC). These changes in CVR/CVC occur with steady-state changes in CA which can be described by Lassen’s curve. However, it is unclear what drives phase differences between pressure and flow at the respiration rate of around 0.2 Hz (12 breaths per minute). Quantifying phase differences at the physiologic respiration rate could be useful to gain a better understanding of the effects of CA and as a potential clinical monitoring tool. In this work, we looked at phase differences between arterial blood pressure (ABP) and intracranial pressure (ICP) measured with invasive pressure sensors, which serve as surrogates for CPP and CBF, to investigate how Arg(ABP)-Arg(ICP) change at the respiration rate as a function of the CPP. We quantify how Arg(ABP)-Arg(ICP) changes with respect to CPP after low-frequency oscillations, respiratory induced oscillations, and with oscillations driven by the heart rate. In each frequency regime, the trends in phase differences between Arg(ABP)-Arg(ICP) are unique with respect to CPP. At the respiration rate, the trends in Arg(ABP)-Arg(ICP) did not completely follow those predicted by a dynamic CA response or by CVC/CVR, thus we believe that there is a combination of effects influencing the phase difference between Arg(ABP)-Arg(ICP) at the respiration frequency. We also explore whether this response could be monitored completely non-invasively using near infrared spectroscopy (NIRS). We use Arg(ΔHbT)-Arg(ΔHbO) as surrogates for CPP and CBF and see a similar response of phase differences with respect to CPP at the respiration rate.
SignificanceCerebrovascular impedance (CVI) is related to cerebral autoregulation (CA), which is the mechanism of the brain to maintain near-constant cerebral blood flow (CBF) despite changes in cerebral perfusion pressure (CPP). Changes in blood vessel impedance enable the stabilization of blood flow. Due to the interplay between CVI and CA, assessment of CVI may enable quantification of CA and may serve as a biomarker for cerebral health.AimWe developed a method to quantify CVI based on a combination of diffuse correlation spectroscopy (DCS) and continuous wave (CW) near-infrared spectroscopy (NIRS). Data on healthy human volunteers were used to validate the method.ApproachA combined high-speed DCS-NIRS system was developed, allowing for simultaneous, noninvasive blood flow, and volume measurements in the same tissue compartment. Blood volume was used as a surrogate measurement for blood pressure and CVI was calculated as the spectral ratio of blood volume and blood flow changes. This technique was validated on six healthy human volunteers undergoing postural changes to elicit CVI changes.ResultsAveraged across the six subjects, a decrease in CVI was found for a head of bed (HOB) tilting of −40 deg. These impedance changes were reversed when returning to the horizontal (0 deg) HOB baseline.ConclusionsWe developed a combined DCS-NIRS system, which measures CBF and volume changes, which we demonstrate can be used to measure CVI. Using CVI as a metric of CA may be beneficial for assessing cerebral health, especially in patients where CPP is altered.
SignificanceIntracranial pressure (ICP) measurements are important for patient treatment but are invasive and prone to complications. Noninvasive ICP monitoring methods exist, but they suffer from poor accuracy, lack of generalizability, or high cost.AimWe previously showed that cerebral blood flow (CBF) cardiac waveforms measured with diffuse correlation spectroscopy can be used for noninvasive ICP monitoring. Here we extend the approach to cardiac waveforms measured with near-infrared spectroscopy (NIRS).ApproachChanges in hemoglobin concentrations were measured in eight nonhuman primates, in addition to invasive ICP, arterial blood pressure, and CBF changes. Features of average cardiac waveforms in hemoglobin and CBF signals were used to train a random forest (RF) regressor.ResultsThe RF regressor achieves a cross-validated ICP estimation of 0.937r2, 2.703-mmHg2 mean squared error (MSE), and 95% confidence interval (CI) of [ − 3.064 3.160 ] mmHg on oxyhemoglobin concentration changes; 0.946r2, 2.301-mmHg2 MSE, and 95% CI of [ − 2.841 2.866 ] mmHg on total hemoglobin concentration changes; and 0.963r2, 1.688 mmHg2 MSE, and 95% CI of [ − 2.450 2.397 ] mmHg on CBF changes.ConclusionsThis study provides a proof of concept for the use of NIRS in noninvasive ICP estimation.
Current standard-of-care methods for measuring intracranial pressure (ICP) are highly invasive. To overcome this limitation, we recently demonstrated non-invasive quantification of ICP in an animal model using morphological analysis of the pulsatile cerebral blood flow (CBF) measured with Diffuse Correlation Spectroscopy. Here, we present results from a pilot study in pediatric patients admitted to an intensive care unit. We show that the CBF pulsatile waveform changes with ICP. Using a regression forest-based machine learning algorithm on a cohort of patients (n>15) we demonstrate that ICP extraction in humans can be possible, suggesting the potential for successful clinical translation in future.
Near-infrared spectroscopy was used to observe the cerebral hemodynamic response of freedivers during single breath-hold training dives. We observed variability in hemodynamic progression, and heartbeat shape changes with diving depth.
Functional near-infrared spectroscopy is successfully used to measure brain activation to visual, auditory, and tactile stimuli in non-domesticated grey seals. The results encourage further investigation of cognition in free-ranging animals.
We have previously developed a non-invasive intracranial pressure sensor based on cerebral blood flow cardiac pulse shape changes. Here, we present steps towards clinical translation in pediatric patients in critical care.
Cerebrovascular Autoregulation failure is known to allow drastic changes in cerebral blood flow in cases of extreme Cerebral Perfusion Pressure (CPP) or Intracranial Pressure (ICP). Brain pathologies (such as traumatic brain injury, hydrocephalus, stroke, etc.) which alter CPP and ICP are also known to show impaired neurovascular coupling. We analyzed these characteristic changes in neurovascular coupling in a model to develop a non-invasive diagnostic marker of autoregulation failure using EEG, Near-Infrared Spectroscopy and Diffuse Correlation Spectroscopy.
Intracranial pressure (ICP) is an important metric in the management of severe head injury. We show alternatives to today’s standard of highly invasive measurement devices using near-infrared spectroscopy and diffuse correlation spectroscopy to create a real-time ICP monitor. The algorithms were developed and tested in an animal model. First results of a clinical validation will be presented.
Vascular impedance is a frequency dependent quantity relating a vascular compartment's flow dynamics to pressure changes. Although vascular impedance has been investigated in larger arteries, probing smaller arterioles using similar techniques has been difficult due to their small cross-sectional area. Here we show how vascular impedance can be quantified based on pulsatile data from cerebral measurements using diffuse correlation spectroscopy (DCS) as well as near-infrared spectroscopy (NIRS). Results from head of bed tilting in healthy volunteers will be presented, showing that quantification of vascular impedance is possible. Applications and the relationship to cerebral autoregulation will be discussed.
Measuring intracranial pressure (ICP) is typically a highly invasive procedure, in which a ventricular catheter or pressure sensor is placed into the brain. To improve the availability of ICP measurements in non-intensive care patients and research and to reduce the invasiveness and underlying risks of ICP sensing, we developed a non-invasive method to measure ICP with Diffuse Correlation Spectroscopy (DCS) and machine learning. ICP baseline changes were induced in non-human primates (Macaca mulatta) through adjusting the height of a saline reservoir connected to the lateral ventricle via a catheter. ICP was precisely measured with an invasive parenchymal pressure sensor. Cerebral blood flow (CBF) was measured with DCS. The DCS system was operated by a software correlator able to resolve cardiac pulse waves at a sampling rate of 100Hz. To increase signal-to-noise ratio, multiple cardiac pulse waves in CBF were averaged based on systolic peak maximum in invasively measured arterial blood pressure. We hypothesized that the cerebral blood flow pulse waves will change their shape with increasing ICP. The shape of the curve was expressed in numerical features and passed into a regression forest training algorithm. Preliminary results show successful prediction of underlying ICP baselines by the decision forest in one animal. The prediction of non-invasive ICP was achieved with a sampling rate of 1 Hz, an equivalent of about 120 averaged pulses. A larger data set for increased generalizability is the next step to push this approach further.
Diffuse correlation spectroscopy (DCS) is an optical method for non-invasive measurements of blood flow in deep tissue microvasculature, such as the brain, without the need for tracers or ionizing radiation. The technique relies on determining temporal autocorrelations of light intensity fluctuations which arise due to time changing speckle patterns of moving scatterers when illuminated by a long coherence length laser. Measurements of blood flow using DCS have extensively been validated and have found some clinical translation already. High temporal resolution by fast sampling of the autocorrelation curves has recently been achieved by software based correlators. Here we demonstrate a new software correlator approach which uses components that are an order of magnitude cheaper than current approaches. We will present on the instrument design, as well as measurements of pulsatile blood flow on healthy volunteers. We will show blood flow measurements with a signal bandwidth of 50Hz and present on signal to noise ratios (SNR) of extracted pulse waveforms as a function of sampling rate. We will show how using an EKG based timing of the signal for averaging increases the fidelity of extracting the blood flow waveform even in low SNR environments. We will further present results of the pulsatile waveforms and the latency of the dicrotic notch as affected by posture changes in healthy volunteers.
Guiding treatment in traumatic brain injury based on managing and optimizing cerebral perfusion pressure, which is the difference between mean arterial blood pressure and intracranial pressure (ICP), has been demonstrated to improve patient outcome. However, this requires ICP to be measured, which currently is only possible by placing pressure probes inside the brain. The feasibility of optical systems to measure ICP non-invasively has shown preliminary promising evidence of feasibility. To pursue the goal of non-invasive ICP acquisition further, an understanding of the influence of different pressure changes on the brain and their hemodynamic response is necessary. To investigate the frequency content of hemodynamic reactions to pressure changes in both ICP as well as arterial blood pressure (ABP), we induced changes of both pressures in non-human primates. We then demonstrate that ABP and ICP changes both influence cerebral blood flow and hemoglobin concentrations, measured with diffuse correlation spectroscopy (DCS) and near-infrared spectroscopy (NIRS), respectively. We found that the magnitude of induced oscillations is dependent on the frequency of the oscillation. Our data suggests, changes in ABP and ICP influence the hemodynamics differently, which we can use as a basis for non-invasive ICP measurements.
Cerebral microvascular changes are influenced by intracranial pressure (ICP) as well as mean arterial blood pressure (MAP). The mechanism maintaining blood flow despite changes in either pressure is called cerebral autoregulation. This mechanism is known to be impaired in many diseases, including traumatic brain injury and stroke. Maintaining adequate cerebral blood flow and autoregulation is known to improve long term patient outcomes. However, the influence on the microvasculature and autoregulation of blood pressure vs. fluid increase, hence intracranial pressure, is not well understood. Furthermore, while blood pressure changes can readily be measured, intracranial pressure sensors are invasive and there is a need to overcome this invasiveness. We have recently shown that changes in cerebral perfusion pressure, which is the difference between blood pressure and intracranial pressure, can be correlated to total hemoglobin concentration, as measured non-invasively with near-infrared spectroscopy (NIRS) in non-human primates. These results showed that non-invasive intracranial pressure monitoring should be possible by means of vascular changes as measured with NIRS. In order to quantify autoregulation and differentiate between blood pressure and fluid increase driven vascular changes, we collected data on non-human primates. The primates’ brains were cannulated to induce rapid changes in ICP. Exsanguination was performed to reduce blood pressure. Data was collected with a combined frequency domain NIRS (OxiplexTS, ISS Inc.) and diffuse correlation spectroscopy (DCS) system for measuring hemoglobin concentration changes as well as blood flow changes, respectively. We will present on the experimental implementation as well as data analysis for quantifying cerebral autoregulation.
The mechanism that maintains a stable blood flow in the brain despite changes in cerebral perfusion pressure (CPP), and therefore guaranties a constant supply of oxygen and nutrients to the neurons, is known as cerebral auto-regulation (CA). In a certain range of CPP, blood flow is mediated by a vasomotor adjustment in vascular resistance through dilation of blood vessels. CA is known to be impaired in diseases like traumatic brain injury, Parkinson’s disease, stroke, hydrocephalus and others. If CA is impaired, blood flow and pressure changes are coupled and thee oxygen supply might be unstable. Lassen’s blood flow auto-regulation curve describes this mechanism, where a plateau of stable blood flow in a specific range of CPP corresponds to intact auto-regulation. Knowing the limits of this plateau and maintaining CPP within these limits can improve patient outcome. Since CPP is influenced by both intracranial pressure and arterial blood pressure, long term changes in either can lead to auto-regulation impairment. Non-invasive methods for monitoring blood flow auto-regulation are therefore needed. We propose too use Near infrared spectroscopy (NIRS) too fill this need. NIRS is an optical technique, which measures microvascular changes in cerebral hemoglobin concentration. We performed experiments on non-human primates during exsanguination to demonstrate that thee limits of blood flow auto-regulation can be accessed with NIRS.
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