Special Section on the BRAIN Initiative

Diffuse correlation spectroscopy for measurement of cerebral blood flow: future prospects

[+] Author Affiliations
Erin M. Buckley

Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts 02129

Ashwin B. Parthasarathy

University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania 19104

P. Ellen Grant

Boston Children’s Hospital, Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston, Massachusetts 02115

Arjun G. Yodh

University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania 19104

Maria Angela Franceschini

Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts 02129

Neurophoton. 1(1), 011009 (Jun 20, 2014). doi:10.1117/1.NPh.1.1.011009
History: Received April 3, 2014; Revised May 4, 2014; Accepted May 7, 2014
Text Size: A A A

Open Access Open Access

Abstract.  Diffuse correlation spectroscopy (DCS) is an emerging optical modality used to measure cortical cerebral blood flow. This outlook presents a brief overview of the technology, summarizing the advantages and limitations of the method, and describing its recent applications to animal, adult, and infant cohorts. At last, the paper highlights future applications where DCS may play a pivotal role individualizing patient management and enhancing our understanding of neurovascular coupling, activation, and brain development.

Figures in this Article

Cerebral blood perfusion is a biomarker of brain health and function. Functioning neurons need adenosine triphosphate (ATP), and ATP is produced almost entirely through oxidative metabolism. Therefore, if the oxygen supply is insufficient, then energy-dependent neuronal processes cease, and irreversible cellular damage ensues. Adequate cerebral blood flow (CBF) ensures the delivery of oxygen and needed substrates to tissue, and it also ensures removal of metabolic waste products. Thus, the quantification of CBF is useful for diagnosis and management of any brain injury or disease associated with ischemia or inadequate vascular autoregulation. Further, the quantification of CBF in healthy subjects, for example with respect to age or during functional activation, can elucidate connections between vascular physiology and neurophysiology, and these normative parameters, in turn, improve our ability to recognize and understand a diseased brain.

This paper is focused on a relatively new blood flow measurement modality that utilizes the intensity fluctuations of near-infrared (NIR) light to noninvasively quantify CBF at the bedside. This technology is called diffuse correlation spectroscopy (DCS). Initial research in animals and in humans has validated DCS indices of CBF and has demonstrated its potential as a preclinical and neuroscience research tool and as a clinical monitoring tool. Herein, we present a brief background about DCS instrumentation and the underlying theory, and we summarize its most prominent cerebral monitoring applications to date. Then, we discuss the advantages and limitations of current technology and indicate directions for improvement. Finally, we suggest clinical and neuroscience applications that could benefit significantly from this novel technology in the future.

As noted above, DCS is an emerging optical modality to monitor CBF. In recent years, excellent reviews about the theory, instrumentation, validation, and clinical applications of DCS have been published, and the interested reader will enjoy these papers.15 DCS detects blood flow by quantifying temporal fluctuations of light fields emerging from the tissue surface. Typically, these light fields are generated by illuminating the brain surface with NIR laser light; some of the NIR light propagates through the scalp and skull and into the brain where it is scattered by moving red blood cells in tissue vasculature before emerging from the tissue surface. This “dynamic” scattering from moving cells [Fig. 1(a)] causes the detected intensity to temporally fluctuate [Fig. 1(b)], and the time scale of these fluctuations is quantified by the intensity temporal autocorrelation function of the collected light [see Fig. 1(c)].

Graphic Jump LocationF1 :

(a) Schematic of typical DCS instrumentation that consists of a long-coherence length source coupled to a multimode fiber for light delivery to the tissue, photon-counting detector(s), and an autocorrelator board that computes the intensity of the autocorrelation function, g2(τ), based on photon arrival times (b), (c) Sample g2(τ) curves obtained over the frontal cortex in a subject under baseline conditions (black) and under hypercapnia (3% inspired carbon dioxide, gray). The increased decay rate of g2(τ) during hypercapnia reflects the increase in CBF by vasodilation.

In practice, the correlation diffusion equation,6,7 or its integral analog,8,9 is employed to fit the measured autocorrelation function to simple physical models and thereby extracts a cerebral blood flow index (CBFi, cm2/s). Numerous studies in humans and in animals (recently reviewed in2) have shown that the relative changes of CBFi over time, that is, relative cerebral blood flow (rCBF), agree very well with relative changes in CBF measured by “gold standard” blood flow measurement techniques, such as arterial spin-labeled MRI (ASL-MRI) and fluorescent microspheres as shown in Fig. 2(a). Further, in neonatal piglets and humans, absolute CBFi has been shown to correlate strongly with CBF measured by bolus tracking time-domain near-infrared spectroscopy (NIRS) and with CBF measured by phase-encoded velocity mapping magnetic resonance imaging (MRI) in the sagittal sinus [see Fig. 2(b)], respectively.1012 Additionally, the combination of NIRS-measured hemoglobin oxygen saturation (SO2) and DCS-measured CBFi has been utilized to derive quantitative information about cerebral oxygen metabolism (CMRO2).1,11,13

Graphic Jump LocationF2 :

Selected validation of DCS measured CBFi against other CBF modalities. (a) Relative changes in CBF measured by DCS (rCBFDCS) is highly correlated with relative CBF measured with fluorescent microspheres (rCBFFM);14 (b) Absolute CBFi correlates well with absolute CBF measured with phase encoded velocity mapping MRI in neonates.12

DCS instrumentation consists of three main components: a long-coherence-length (>5m) laser operating in the NIR to deliver light to the tissue; single photon counting avalanche photodiode (APD) detectors that output an electronic pulse for every photon received; and a photon correlator that keeps track of the arrival times of all photons detected by the APDs and derives an intensity correlation function from the temporal separations of all pairs of photons (see Fig. 1). The correlator has traditionally been a piece of hardware, but with improved computation power and speed, software computation of temporal correlation functions will soon become increasingly prevalent. These components are readily housed in lightweight, portable enclosures that can be incorporated into a bedside clinical monitoring unit. To date, most DCS instruments have been home-built; however, recently several companies (Hemophotonics, ISS Inc., Flox Medical, Canon USA) have initiated commercialization of DCS systems.

Advantages

DCS offers several advantages over other methods that measure CBF. Unlike traditional clinical CBF measurement modalities, such as xenon-133 computed tomography (xenon-CT) and positron emission tomography, DCS does not expose the patient to ionizing radiation. Furthermore, unlike safer new methods such as ASL-MRI, DCS is relatively inexpensive, portable, and the DCS measurement can easily be carried out multiple times during a patient’s hospital stay. Lastly, in contrast to transcranial Doppler ultrasound, which probes blood flow in major vessels, DCS can be used at any location on the patient’s head to assess regional cortical microvascular flow.

While these advantages of DCS are important for patients of all ages, they are most significant for infants, a population for whom access to MRI is limited. Indeed, DCS holds the potential to fill a critical niche for the assessment of neonatal brain health, brain development, autoregulation, responses to physiological manipulation or therapeutic intervention, and functional responses to sensory and cognitive stimuli. Because measurements can be performed in a rapid, noninvasive, and low-risk fashion using a portable bedside instrument without anesthesia or sedation, DCS provides distinct advantages over other perfusion modalities used in infants, such as ASL-MRI, which requires transport from clinical units to the radiology department, sedated or sleeping patients to limit motion, and which suffers from noise when CBF is low (as is typically seen in infants).

DCS instrumentation is easy to assemble and requires only one wavelength that can be chosen based on laser availability and cost (e.g., by contrast, NIRS requires multiple wavelengths within the NIR tissue absorption window). In addition, unlike the NIRS technique, DCS measurements do not require reference phantoms for quantification. Finally, as noted above, when DCS is integrated with NIRS, the combined instrumentation derives a more robust picture of brain health, i.e., via measurement of tissue oxygenation, cerebral blood flow, and oxygen metabolism.

In addition to the direct advantages of the diffuse optical technology over the existing methods, DCS theoretical concepts can be adapted to improve other blood flow imaging techniques. For example, the elements and concepts from DCS have recently found use in more surface-sensitive CBF imaging methods, such as laser speckle contrast imaging (LSCI);1520 further, the combination of DCS theory, spatial frequency-domain imaging, and LSCI has led to quantitative measurements of the physical properties of liquids21 and depth resolved blood flow measurements.22 Recent experiments have also increased the depth sensitivity of blood flow imaging using DCS concepts.23

Limitations

Numerous validation studies in both adults and children have shown that the relative changes in CBFi over time (i.e., rCBF) measured by DCS agree quite well with rCBF measured by other modalities (see Fig. 2 and 2). However, the quantification of absolute CBFi remains to be determined across a wide range of subjects and tissue types. To date, the correlations between CBFi and absolute CBF (measured by other modalities) have only been observed in young piglets and in human neonates, i.e., population cohorts in whom the ratio of extracerebral thickness to the source-detector separation is approximately 0.25 or smaller. In older pediatric populations and in adults, the contribution of the thick scalp and skull layers to CBFi cannot be neglected; in these cases, the absolute quantification of CBFi will most likely require the use of multilayered modeling.24 Moreover, because of the relevant scalp contribution in adults, it is desirable to account for probe pressure effects,25 especially when the source-detector separation is less than 2.5 cm. In neonates and in small animals, these factors play a relatively small role, most likely because the ratio of extracerebral thickness to source-detector separation is small, and it is therefore more likely that the detected photons will travel through the cortex.

Another important parameter needed for quantification of CBFi is the tissue reduced scattering coefficient, μs’, i.e., the reciprocal of the photon random walk step length. Although CBFi is influenced by both tissue absorption and scattering coefficients, μa and μs’, respectively, uncertainties in μa lead to relatively small errors in CBFi (<20%), whereas uncertainties in μs’ can translate to substantial errors in CBFi.26 Thus, when quantifying an absolute CBFi, it is desirable to combine DCS with a method to determine absolute μa and μs’ for the tissue of interest, e.g., frequency- or time-domain NIRS. We note, however, that in neonates, Jain et al. observed a strong correlation between CBFi and CBF in the sagittal sinus despite assuming a fixed μs’ across the entire cohort [see Fig. 2(b)].12 This agreement, at least in part, may have been a result of the homogeneity of the patient cohort, i.e., all were <1 week neonates with congenital heart defects. Clearly, in future studies it will be better to measure μs’ in all subjects whenever possible. (Note: the effects of uncertainties in μa and μs’ on CBFi are diminished when monitoring relative changes in CBFi over time, since these tissue parameters change little except in cases of extreme interval blood loss/gain, edema, etc).

As is the case with other optical modalities, the penetration depth of DCS, while larger than wide-field optical methods, such as LSCI, is limited compared to traditional clinical modalities, such as MRI and CT. Like NIRS, a common rule-of-thumb for DCS is that it samples a banana-shaped region underneath the optical probe that is roughly one-third to one-half of the source-detector separation;1 thus, typical DCS experimental setups with source-detector separations of 2 to 3 cm are sensitive to superficial cortical regions. CBF in deep brain structures has not been quantified by DCS. To increase the penetration depth, larger source-detector separations are required. The use of larger source-detector separation configurations, however, is challenging since DCS optimally requires single-mode detection fibers for measurement of the intensity autocorrelation function. The throughput of single-mode fibers is relatively low and leads to a low signal-to-noise ratio (SNR), especially in adults where large source-detector separations (>2cm) are needed to detect cerebral signals. This low SNR becomes even more problematic in the presence of hair and/or dark skin; it can increase acquisition times and reduce temporal resolution. To overcome the issue of low SNR, most groups employ multiple detector fibers bundled together in the same position, and then the autocorrelation curves from all detectors are averaged to derive a single curve. Unfortunately, this strategy is a relatively expensive option that requires many photon counting detectors. Alternative strategies to improve SNR include increased light collection through improved collection optics, such as those presented by Dietsche et al.,27 or the use of few-mode (rather than single mode) detection fibers.28 Additionally, improved SNR can be achieved by increasing the amount of light delivered to the tissue;29 however, the ANSI standards for skin exposure must be considered, and a large diameter source fiber and/or diffuser should be used to keep light exposure levels within safe limits.

Finally, a significant limitation of DCS is its susceptibility to motion artifacts. Of course, most neuroimaging techniques are sensitive to motion artifacts, hence this issue is not unique to DCS. To understand this problem more deeply, recall that blood flow contrast in DCS primarily derives from intensity fluctuations due to moving particles in the sampled volume. Therefore, the motion of the probe when it is in contact with the patient can cause additional fluctuations due to static tissue (and other) structures that can add noise (random and systematic) to estimates of CBFi. In practice, securing the optical probes as firmly as possible to the subject’s head has reduced motion artifacts. Looking forward, DCS can employ the same analytical approaches used in functional NIRS to account and filter for motion (principle component analysis, wavelet analysis, spline interpolation, etc.30,31) and thereby eliminate artifacts via improvements in optical probes.32 On the experimental side, Yucel et al. have had excellent recent success in reducing motion artifacts in NIRS data by gluing optodes to the tissue surface with collodion, a glue used with electroencephalogram (EEG) electrodes.32 This approach may also be feasible for DCS, although it may be more practical for utilization in critically ill patients when the potential benefits from cerebral blood flow monitoring outweigh the annoyances of gluing fibers. Additionally, recent approaches with DCS in muscle studies during exercise have successfully employed a dynamometer to gate DCS acquisition and partially remove the effects of motion;33,34 variants of this approach might be useful for the brain.

Animals

Animal models have provided a rich environment for the validation of DCS. Early studies focused on measurement and validation of relative changes in CBF with time (rCBF): Zhou et al. used a closed head injury neonatal piglet model to demonstrate excellent one-to-one agreement of DCS changes in CBF against the changes in CBF measured with fluorescent microspheres [see Fig. 2(a)];14 Carp et al. validated rCBF measured by DCS in a rodent model against ASL-MRI.35 Recent work has also shown agreement between absolute CBFi with absolute CBF measured with bolus tracking time-domain NIRS using indocyanine green dye in piglets. Diop et al. calibrated DCS CBFi to physiologic units (ml/min/100g) in piglets,10 while Verdecchia et al. performed a similar experiment to calibrate CBFi for individual piglets during various physiological manipulations.13

In addition to these validation studies, the quantitative and noninvasive aspects of DCS CBF measurements are very attractive for a variety of animal studies. In rodents, DCS offers the possibility of deep tissue CBF quantification without the need for thinning or removal of the skull, thus leaving the brain unperturbed (i.e., as opposed to more traditional methods of CBF measurement in animals such as laser Doppler flowmetry and laser speckle contrast imaging). DCS has been used in animal models to study baseline and activated hemodynamics as well as related metabolic changes.36 DCS has also been used to study diseases such as ischemic stroke,37,38 phenomena such as cortical spreading depressions,39 and CBF responses to different anesthetics and carbon dioxide.40 Additionally, DCS has been used to extract depth-resolved spatial maps of blood flow.37,39 In sum, these studies have demonstrated the potential of DCS in preclinical and neuroscience animal studies. Arguably, it appears to be a more robust and quantitative measure of CBF than laser Doppler (which has limited depth penetration and poor repeatability), and its validation has opened the possibility for future longitudinal studies due to the noninvasive nature of the technique.

Adults

Although DCS has been largely restricted to measures of rCBF in adults due to difficulties in absolute baseline quantification (see Sec. 2.2), the above-mentioned advantages (see Sec. 2.1) have resulted in numerous preliminary studies demonstrating the clinical potential of DCS. DCS has been successfully applied to monitor rCBF during clinical interventions, such as postural manipulation or blood pressure regulation in patients with ischemic stroke,41 traumatic brain injury, and subarachnoid hemorrhage.42,43 These works have demonstrated the need (and potential) for individualized monitoring of cerebral hemodynamics during therapeutic interventions. In essence, we are discovering that the standard treatment protocols designed to optimize cerebral perfusion in damaged tissue do not always yield the desired result; this limitation of the standard protocols is understandable in light of the often highly heterogeneous nature of these brain injuries, and it points to the need for more individualized management (see Fig. 3). In addition, DCS has been used to detect acute spinal cord ischemia in sheep models,44 to assess CBF changes due to vocal stuttering,45 and to elucidate the effects of normal aging on posture manipulations.46 Per new therapeutics, DCS has been used to characterize the cerebral hemodynamic responses due to transcranial magnetic stimulation (TMS) in an effort to elucidate the mechanisms of TMS.47 Finally, per functional activation studies, DCS has shown spatiotemporal sensitivity to finger tapping48 and visual stimulation,49,50 thereby demonstrating its utility for basic neuroscience/physiological studies in patients in a natural environment.

Graphic Jump LocationF3 :

Relative changes in cerebral blood flow measured with DCS during postural manipulation of head-of-bed (HOB) position from 30 to 5° (inset) for (a) a typical healthy subject, (b) a stroke patient who demonstrated the typical response of impaired autoregulation in the injured hemisphere when HOB is lowered, and (c) a stroke patient who demonstrated a paradoxical response in the injured hemisphere during the lowering of HOB, seen in approximately 20% of stroke patients. (a) Left hemisphere in dark gray, right in light gray, (b,c) contralesional in dark gray, ipsalesional in light gray. Figure courtesy of Mesquita et al.3

By directly addressing some of the limitations noted above (Sec. 2.2), it should be possible to expand the use of DCS for clinical monitoring in adults. Improvements to increase SNR and temporal resolution will help the development of functional experiments in adults. Lower costs for sources and detectors should permit whole brain functional imaging experiments that will start to investigate multiregion evoked CBF and CMRO2 changes as well as global resting state CBF and CMRO2 for exploration of regional functional activity and functional connectivity in the adult brain.

Infants

Infants and young children are ideal populations to study with DCS, because they have thinner extracerebral layers than adults. Thus, in this population the community has observed higher SNR, excellent reproducibility of CBFi (i.e., within 10% to 15%11), and greater sensitivity to cortical tissue as compared to adults. Further, infants often have less hair than adults, a mundane but important factor that improves SNR.

To date, neonatal studies with DCS have branched in many directions. In contrast to adults, where the majority of clinical research has focused on changes over time caused by relatively brief interventions (i.e., interventions of order 1 h), neonatal experimental results encompass both episodic monitoring as well as periodic quantification of absolute CBFi. Episodic monitoring has been used to quantify the response to hypercapnia,51 postural manipulation,52 drug delivery,53 surgical intervention,54 therapeutics,53,55 and functional activation.56 These studies have provided previously unknown and very valuable information about CBF and CMRO2 responses to intervention. For example, Buckley et al. observed significant and substantial dose-dependent increases in CBF following bolus administration of sodium bicarbonate to correct metabolic acidosis.53 Recognizing these dose-dependent effects may be especially important in treating patient populations where the risks of rapid fluctuations in CBF may lead to subsequent brain injury and thus limit the benefits of correcting the underlying acidemia. Dehaes et al. have observed substantial decreases in CBFi and CMRO2i in hypoxic ischemic injured neonates under therapeutic hypothermia.55 The ability to measure CMRO2 during therapeutic hypothermia and assess its dynamic may help to individually optimize and guide this therapy.

Further, Roche-Labarbe et al. recently demonstrated the feasibility of DCS functional studies in infants.56 By combining NIRS with DCS, both CBF and CMRO2 changes are measured (see Fig. 4). Understanding metabolic and vascular functional measures, as well as their coupling relationships, holds tremendous potential for assessment of perceptual and cognitive development and for deriving early indications of neuropsychological and behavioral deficits in at-risk populations.

Graphic Jump LocationF4 :

Fractional changes of oxy- and deoxy-hemoglobin concentrations (rHbO and rHbR, respectively), as well as cerebral blood flow (rCBF) and cerebral oxygen metabolism (rCMRO2i) measured with NIRS and DCS overthe left somatosensory cortex in preterm infants during 5 seconds of gentle stimulation of the hand, indicated by the gray shaded region.56

Following numerous validation studies,2 the periodic measures of CBFi have been used to investigate changes in cerebral hemodynamics over the first month of development in term and preterm infants,11,57 as well as regional and hemispheric differences in cortical flow.58 These works elucidate the normal evolution of SO2, CBF, and CMRO2 in the developing neonate, which is critical to understanding brain in disease states. Furthermore, the works by Roche-Labarbe et al.57 and Dehaes et al.,55 in particular, highlight the relative insensitivity of SO2 alone, i.e., the standard parameter provided by commercially available stand-alone NIRS devices, as a measure of brain health. These works demonstrate the obvious advantage that DCS adds in providing a more complete picture of neonatal development through quantification of CBF and CMRO2.

From a clinical perspective, because DCS is a safe, inexpensive, noninvasive, and portable modality, it can be used as a bedside monitor of cerebral perfusion in both the intensive care unit (ICU) and the operating room (OR). Commercially available continuous-wave NIRS devices that estimate regional cerebral oxygenation (rSO2) are already in widespread use in ICUs and ORs. DCS provides valuable complementary information to NIRS per microvascular CBF and, therefore, regional oxygen delivery. The combination of NIRS with DCS also enables the quantification of regional CMRO2, a parameter that many believe is a direct measure of neuronal health;59 this information provides the clinician with a more complete assessment of cerebral health and physiology than either SO2 or CBF alone.

As an example of how the combination of NIRS and DCS can be used, we note that SO2 can increase either with increased neuronal activity (i.e., when supply exceeds neuronal demand) or with decreased neuronal activity (i.e., when neuronal oxygen consumption is decreased). Therefore, when increases in SO2 are noted by NIRS, it is vital to determine if the changes are associated with increased delivery or decreased oxygen consumption; DCS measures of CBF combined with NIRS can help assess increases/decreases in neuronal activity. In addition, if decreases in SO2 are observed, simultaneous quantification of CBF can help clinicians ascertain whether these decreases are due to the failure of oxygen delivery (as when CBF decreases due to decreased cardiac output) or due to increased oxygen consumption. Looking forward, the potential for DCS combined with NIRS to enable ICU and OR management of subjects based on optimizing regional cerebral CMRO2 should be explored as an alternative to strategies based on a secondary systemic and/or cerebral parameters, such as SO2.

Per therapy development, DCS can be used in both animal and human studies, especially for therapies (e.g., drugs, etc.) aimed at altering cerebral microvascular perfusion. This approach would see first testing in animal studies, which, in turn, would then set the stage for DCS monitoring in human trials.

Beyond the immediate potential for DCS in the clinic, DCS and other diffuse optical tools should be expected to help scientists increase our understanding of neurovascular coupling and early brain development. The ability to monitor neurovascular responses in more natural environments, outside the noisy MRI scanner, will enable the use of more complex stimulation paradigms. In addition, monitoring CBF with DCS or the entire suite of CMRO2, SO2, and CBF (when combined with NIRS) will enable us to better understand the nature of the neurovascular response. The ability to quantify CMRO2 during functional changes, for example, will permit better interpretation of functional response, since oxygen metabolism changes will be less influenced by systemic physiology.

We thank David Boas, Turgut Durduran, Stefan Carp, Rickson Mesquita, Wesley Baker, Ivy Lin, Nadege Roche-Labarbe, Mathieu Dehaes, Linda van Marter, Harsha Radakrishnan, Malavika Chandra, Meeri Kim, John Detre, Joel Greenberg, and Daniel Licht for valuable discussions. We also gratefully acknowledge support from the National Institutes of Health through Grant Nos. R01-NS060653, P41-EB015893, P41-RR14075, R01-HD042908, and R01-EB001954.

Durduran  T. et al., “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys.. 73, (7 ), 1 –43 (2010). 0034-4885 CrossRef
Durduran  T., Yodh  A. G., “Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement,” Neuroimage. 85, (Part 1 ), 51 –63 (2014). 1053-8119 CrossRef
Mesquita  R. C. et al., “Direct measurement of tissue blood flow and metabolism with diffuse optics,” Philos. Transact. Series A. Math. Phys. Eng. Sci.. 369, (1955 ) 4390 –4406 (2011). 1364-503X CrossRef
Yu  G., “Diffuse correlation spectroscopy (DCS): a diagnostic tool for assessing tissue blood flow in vascular-related diseases and therapies,” Curr. Med. Imag. Rev.. 8, (3 ), 194 –210 (2012). 1573-4056 CrossRef
Yu  G., “Near-infrared diffuse correlation spectroscopy (DCS) for assessment of tissue blood flow,” Chapter 10 in Handbook of Biomedical Optics. , Boas  D. A., Pitris  C., Ramanujam  N., Eds., pp. 195 –216,  Taylor and Francis Books, Inc. ,  Boca Raton, Florida  (2011).
Boas  D. A., Campbell  L. E., Yodh  A. G., “Scattering and imaging with diffusing temporal field correlations,” Phys. Rev. Lett.. 75, (9 ), 1855 –1858 (1995). 0031-9007 CrossRef
Boas  D. A., Yodh  A. G., “Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation,” J. Opt. Soc. Am. A.. 14, (1 ), 192 –215 (1997). 0740-3232 CrossRef
Maret  G., Wolf  P. E., “Multiple light scattering from disordered media. The effect of Brownian motion of scatterers,” Z. Physik B. 65, (4 ), 409 –413 (1987). 0722-3277 CrossRef
Pine  D. J. et al., “Diffusing wave spectroscopy,” Phys. Rev. Lett.. 60, (12 ), 1134 –1137 (1988). 0031-9007 CrossRef
Diop  M. et al., “Calibration of diffuse correlation spectroscopy with a time-resolved near-infrared technique to yield absolute cerebral blood flow measurements,” Biomed. Opt. Exp.. 2, (7 ), 2068 –2081 (2011). 2156-7085 CrossRef
Roche-Labarbe  N. et al., “Noninvasive optical measures of CBV, StO2, CBF index, and rCMRO2 in human premature neonates’ brains in the first six weeks of life,” Hum. Brain Mapp.. 31, (3 ), 341 –352 (2010). 1065-9471 CrossRef
Jain  V. et al., “Cerebral oxygen metabolism in neonates with congenital heart disease quantified by MRI and optics,” J. Cereb. Blood Flow Metab.. 34, (3 ), 380 –388 (2014). 0271-678X CrossRef
Verdecchia  K. et al., “Quantifying the cerebral metabolic rate of oxygen by combining diffuse correlation spectroscopy and time-resolved near-infrared spectroscopy,” J. Biomed. Opt.. 18, (2 ), 027007  (2013). 1083-3668 CrossRef
Zhou  C. et al., “Diffuse optical monitoring of hemodynamics in piglet brain with head trauma injury,” J. Biomed. Opt.. 14, (3 ), 034015  (2009). 1083-3668 CrossRef
Durduran  T. et al., “Spatiotemporal quantification of cerebral blood flow during functional activation in rat somatosensory cortex using laser-speckle flowmetry,” J. Cereb. Blood Flow Metab.. 24, (5 ), 518 –525 (2004). 0271-678X CrossRef
Fercher  A. F., Briers  J. D., “Flow visualization by means of single-exposure speckle photography,” Opt. Commun.. 37, (5 ), 326 –330 (1981). 0030-4018 CrossRef
Parthasarathy  A. B., “Laser speckle contrast imaging of cerebral blood flow in humans during neurosurgery: a pilot clinical study,” J. Biomed. Opt.. 15, (6 ), 066030  (2010). 1083-3668 CrossRef
Kazmi  S. M. et al., “Chronic imaging of cortical blood flow using multi-exposure speckle imaging,” J. Cereb. Blood Flow Metab.. 33, (6 ), 798 –808 (2013). 0271-678X CrossRef
Boas  D. A., Dunn  A. K., “Laser speckle contrast imaging in biomedical optics,” J. Biomed. Opt.. 15, (1 ), 011109  (2010). 1083-3668 CrossRef
Dunn  A. K., “Dynamic imaging of cerebral blood flow using laser speckle,” J. Cereb. Blood Flow Metab.. 21, (3 ), 195 –201 (2001). 0271-678X CrossRef
Rice  T. B. et al., “Quantitative determination of dynamical properties using coherent spatial frequency domain imaging,” J. Opt. Soc. Am. A.. 28, (10 ), 2108 –2114 (2011). 0740-3232 CrossRef
Mazhar  A. et al., “Laser speckle imaging in the spatial frequency domain,” Biomed. Opt. Exp.. 2, (6 ), 1553 –1563 (2011). 2156-7085 CrossRef
Bi  R., Dong  J., Lee  K., “Deep tissue flowmetry based on diffuse speckle contrast analysis,” Opt. Lett.. 38, (9 ), 1401 –1403 (2013). 0146-9592 CrossRef
Gagnon  L., “Investigation of diffuse correlation spectroscopy in multi-layered media including the human head,” Opt. Exp.. 16, (20 ), 15514 –15530 (2008). 1094-4087 CrossRef
Mesquita  R. et al., “Influence of probe pressure on the diffuse correlation spectroscopy blood flow signal: extra-cerebral contributions,” Biomed. Opt. Exp.. 4, (7 ), 978 –994 (2013). 2156-7085 CrossRef
Irwin  D. et al., “Influences of tissue absorption and scattering on diffuse correlation spectroscopy blood flow measurements,” Biomed. Opt. Exp.. 2, (7 ), 1969 –1985 (2011). 2156-7085 CrossRef
Dietsche  G. et al., “Fiber-based multispeckle detection for time-resolved diffusing-wave spectroscopy: characterization and application to blood flow detection in deep tissue,” Appl. Opt.. 46, (35 ), 8506 –8514 (2007). 0003-6935 CrossRef
He  L. et al., “Using optical fibers with different modes to improve the signal-to-noise ratio of diffuse correlation spectroscopy flow-oximeter measurements,” J. Biomed. Opt.. 18, (3 ), 037001  (2013). 1083-3668 CrossRef
Lin  P. et al., “Non-invasive optical measurement of cerebral metabolism and hemodynamics in infants,” J. Visual. Exp.. 14, (73 ), e4379  (2013). 1940-087X CrossRef
Yucel  M. A. et al., “Targeted principle component analysis: a new motion artifact correction approach for near-infrared spectroscopy,” J. Innovative Opt. Health Sci.. 7, (2 ), 1350066  (2014). 1793-7205 CrossRef
Cooper  R. J. et al., “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci.. 6, (147 ) (2012). 0887-3658 CrossRef
Yucel  M. A. et al., “Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers,” Neuroimage. 85, (Pt 1 ), 192 –201 (2014). 1053-8119 CrossRef
Shang  Y. et al., “Effects of muscle fiber motion on diffuse correlation spectroscopy blood flow measurements during exercise,” Biomed. Opt. Exp.. 1, (2 ), 500 –511 (2010). 2156-7085 CrossRef
Gurley  K., Shang  Y., Yu  G., “Noninvasive optical quantification of absolute blood flow, blood oxygenation, and oxygen consumption rate in exercising skeletal muscle,” J. Biomed. Opt.. 17, (7 ), 075010  (2012). 1083-3668 CrossRef
Carp  S. et al., “Validation of diffuse correlation spectroscopy measurements of rodent cerebral blood flow with simultaneous arterial spin labeling MRI; towards MRI-optical continuous cerebral metabolic monitoring,” Biomed. Opt. Exp.. 1, (2 ), 553 –565 (2010). 2156-7085 CrossRef
Cheung  C. et al., “In vivo cerebrovascular measurement combining diffuse near-infrared absorption and correlation spectroscopies,” Phys. Med. Biol.. 46, (8 ), 2053 –2065 (2001). 0031-9155 CrossRef
Culver  J. P. et al., “Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia,” J. Cereb. Blood Flow Metab.. 23, (8 ), 911 –924 (2003). 0271-678X CrossRef
Shang  Y. et al., “Diffuse optical monitoring of repeated cerebral ischemia in mice,” Opt. Exp.. 19, (21 ), 20301 –20315 (2011). 1094-4087 CrossRef
Zhou  C. et al., “Diffuse optical correlation tomography of cerebral blood flow during cortical spreading depression in rat brain,” Opt. Exp.. 14, (3 ), 1125 –1144 (2006). 1094-4087 CrossRef
Franceschini  M. A. et al., “The effect of different anesthetics on neurovascular coupling,” Neuroimage. 51, , 1367 –1377 (2010). 1053-8119 CrossRef
Durduran  T. et al., “Transcranial optical monitoring of cerebrovascular hemodynamics in acute stroke patients,” Opt. Exp.. 17, (5 ), 3884 –3902 (2009). 1094-4087 CrossRef
Kim  M. N. et al., “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocrit. Care. 12, (2 ), 173 –180 (2010). 1541-6933 CrossRef
Kim  M. N. et al., “Continuous optical monitoring of cerebral hemodynamics during head-of-bed manipulation in brain-injured adults,” Neurocrit. Care. (2013). 1541-6933 CrossRef
Mesquita  R. C. et al., “Optical monitoring and detection of spinal cord ischemia,” PLoS One. 8, (12 ), e83370  (2013). 1932-6203 CrossRef
Tellis  G. M., Mesquita  R. C., Yodh  A. G., “Use of diffuse correlation spectroscopy to measure brain blood flow differences during speaking and nonspeaking tasks for fluent speakers and persons who stutter,” SIG 4 Perspect. Fluency Fluency Disord.. 21, , 96 –106 (2011). 1940-7599 CrossRef
Edlow  B. L. et al., “The effects of healthy aging on cerebral hemodynamic responses to posture change,” Physiol. Meas.. 31, (4 ), 477 –495 (2010). 0967-3334 CrossRef
Mesquita  R. C. et al., “Blood flow and oxygenation changes due to low-frequency repetitive transcranial magnetic stimulation of the cerebral cortex,” J. Biomed. Opt.. 18, (6 ), 067006  (2013). 1083-3668 CrossRef
Durduran  T. et al., “Diffuse optical measurement of blood flow, blood oxygenation, and metabolism in a human brain during sensorimotor cortex activation,” Opt. Lett.. 29, (15 ), 1766 –1768 (2004). 0146-9592 CrossRef
Jaillon  F. et al., “Activity of the human visual cortex measured non-invasively by diffusing-wave spectroscopy,” Opt. Exp.. 15, , 6643 –6650 (2007). 1094-4087 CrossRef
Li  J. et al., “Transient functional blood flow change in the human brain measured noninvasively by diffusing-wave spectroscopy,” Opt. Lett.. 33, (19 ), 2233 –2235 (2008). 0146-9592 CrossRef
Durduran  T. et al., “Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects,” J. Biomed. Opt.. 15, (3 ), 037004  (2010). 1083-3668 CrossRef
Buckley  E. M. et al., “Cerebral hemodynamics in preterm infants during positional intervention measured with diffuse correlation spectroscopy and transcranial Doppler ultrasound,” Opt. Exp.. 17, (15 ), 12571 –12581 (2009). 1094-4087 CrossRef
Buckley  E. M. et al., “Sodium bicarbonate causes dose-dependent increases in cerebral blood flow in infants and children with single ventricle physiology,” Pediatr. Res.. 73, (5 ), 668 –673 (2013). 0031-3998 CrossRef
Buckley  E. M. et al., “Early postoperative changes in cerebral oxygen metabolism following neonatal cardiac surgery: effects of surgical duration,” J. Thorac. Cardiovasc. Surg.. 145, (1 ), 196 –203 (2013). 0022-5223 CrossRef
Dehaes  M. et al., “Cerebral oxygen metabolism in neonatal hypoxic ischemic encephalopathy during and after therapeutic hypothermia,” J. Cereb. Blood Flow Metab.. 34, (1 ), 87 –94 (2014). 0271-678X CrossRef
Roche-Labarbe  N. et al., “Somatosensory evoked changes in cerebral oxygen consumption measured non-invasively in premature neonates,” Neuroimage. 85, (Pt 1 ), 279 –286 (2014). 1053-8119 CrossRef
Roche-Labarbe  N. et al., “Near-infrared spectroscopy assessment of cerebral oxygen metabolism in the developing premature brain,” J. Cereb. Blood Flow Metab.. 32, (3 ), 481 –488 (2012). 0271-678X CrossRef
Lin  P.-Y. et al., “Regional and hemispheric asymmetries of cerebral hemodynamic and oxygen metabolism in newborns,” Cereb. Cortex. 23, (2 ), 339 –348 (2013). 1047-3211 CrossRef
Boas  D. A., Franceschini  M. A., “Haemoglobin oxygen saturation as a biomarker: the problem and a solution,” Philos. Transact. A, Math. Phys. Eng. Sci.. 369, (1995 ), 4407 –4424 (2011).CrossRef

Erin M. Buckley is a research fellow at the Athinoula A. Martinos Center for Biomedical Imaging. She received her PhD degree in physics from the University of Pennsylvania in 2011, and then spent a year as a postdoctoral fellow in the division of neurology at the Children’s Hospital of Philadelphia before coming to the Martinos Center. She has authored twelve papers on diffuse correlation spectroscopy. In particular, much of her research has focused on the application of DCS in neonates at high risk for brain injury.

Ashwin B. Parthasarathy is a postdoctoral researcher in physics and astronomy at the University of Pennsylvania. He graduated with a PhD in biomedical engineering from the University of Texas at Austin in 2010. He was a postdoctoral researcher at Boston University before moving to Penn, where he researches diffuse correlation spectroscopy methods for stroke applications. He has extensive experience in cerebral blood flow imaging and has made critical contributions to laser speckle contrast imaging.

Arjun G. Yodh is the James M. Skinner professor of science and the director of the Laboratory for Research on the Structure of Matter (LRSM) at the University of Pennsylvania. Physics & Astronomy is his home department and he has a secondary appointment in the Department of Radiation Oncology in the medical school. His current interests span fundamental and applied questions in condensed matter physics, medical and biophysics, and the optical sciences. Areas of ongoing medical-oriented research include soft materials, nonlinear optics, optical microscopy & micromanipulation, diffuse optics, functional imaging and spectroscopy of living tissues, tomography and photodynamic therapy.

Maria Angela Franceschini is a faculty member at the Optics Division of the Martinos Center for Biomedical Imaging at Massachusetts General Hospital and an associate professor at Harvard Medical School. Her research focuses broadly on the development and application of noninvasive optical techniques to studies of the human brain. She is a pioneer in the field NIRS and has made substantial contributions to the modeling, testing and clinical and neuroscience translation of NIRS and DCS.

Biographies of the other authors are not available.

© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation

Erin M. Buckley ; Ashwin B. Parthasarathy ; P. Ellen Grant ; Arjun G. Yodh and Maria Angela Franceschini
"Diffuse correlation spectroscopy for measurement of cerebral blood flow: future prospects", Neurophoton. 1(1), 011009 (Jun 20, 2014). ; http://dx.doi.org/10.1117/1.NPh.1.1.011009


Figures

Graphic Jump LocationF1 :

(a) Schematic of typical DCS instrumentation that consists of a long-coherence length source coupled to a multimode fiber for light delivery to the tissue, photon-counting detector(s), and an autocorrelator board that computes the intensity of the autocorrelation function, g2(τ), based on photon arrival times (b), (c) Sample g2(τ) curves obtained over the frontal cortex in a subject under baseline conditions (black) and under hypercapnia (3% inspired carbon dioxide, gray). The increased decay rate of g2(τ) during hypercapnia reflects the increase in CBF by vasodilation.

Graphic Jump LocationF2 :

Selected validation of DCS measured CBFi against other CBF modalities. (a) Relative changes in CBF measured by DCS (rCBFDCS) is highly correlated with relative CBF measured with fluorescent microspheres (rCBFFM);14 (b) Absolute CBFi correlates well with absolute CBF measured with phase encoded velocity mapping MRI in neonates.12

Graphic Jump LocationF4 :

Fractional changes of oxy- and deoxy-hemoglobin concentrations (rHbO and rHbR, respectively), as well as cerebral blood flow (rCBF) and cerebral oxygen metabolism (rCMRO2i) measured with NIRS and DCS overthe left somatosensory cortex in preterm infants during 5 seconds of gentle stimulation of the hand, indicated by the gray shaded region.56

Graphic Jump LocationF3 :

Relative changes in cerebral blood flow measured with DCS during postural manipulation of head-of-bed (HOB) position from 30 to 5° (inset) for (a) a typical healthy subject, (b) a stroke patient who demonstrated the typical response of impaired autoregulation in the injured hemisphere when HOB is lowered, and (c) a stroke patient who demonstrated a paradoxical response in the injured hemisphere during the lowering of HOB, seen in approximately 20% of stroke patients. (a) Left hemisphere in dark gray, right in light gray, (b,c) contralesional in dark gray, ipsalesional in light gray. Figure courtesy of Mesquita et al.3

Tables

References

Durduran  T. et al., “Diffuse optics for tissue monitoring and tomography,” Rep. Prog. Phys.. 73, (7 ), 1 –43 (2010). 0034-4885 CrossRef
Durduran  T., Yodh  A. G., “Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement,” Neuroimage. 85, (Part 1 ), 51 –63 (2014). 1053-8119 CrossRef
Mesquita  R. C. et al., “Direct measurement of tissue blood flow and metabolism with diffuse optics,” Philos. Transact. Series A. Math. Phys. Eng. Sci.. 369, (1955 ) 4390 –4406 (2011). 1364-503X CrossRef
Yu  G., “Diffuse correlation spectroscopy (DCS): a diagnostic tool for assessing tissue blood flow in vascular-related diseases and therapies,” Curr. Med. Imag. Rev.. 8, (3 ), 194 –210 (2012). 1573-4056 CrossRef
Yu  G., “Near-infrared diffuse correlation spectroscopy (DCS) for assessment of tissue blood flow,” Chapter 10 in Handbook of Biomedical Optics. , Boas  D. A., Pitris  C., Ramanujam  N., Eds., pp. 195 –216,  Taylor and Francis Books, Inc. ,  Boca Raton, Florida  (2011).
Boas  D. A., Campbell  L. E., Yodh  A. G., “Scattering and imaging with diffusing temporal field correlations,” Phys. Rev. Lett.. 75, (9 ), 1855 –1858 (1995). 0031-9007 CrossRef
Boas  D. A., Yodh  A. G., “Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation,” J. Opt. Soc. Am. A.. 14, (1 ), 192 –215 (1997). 0740-3232 CrossRef
Maret  G., Wolf  P. E., “Multiple light scattering from disordered media. The effect of Brownian motion of scatterers,” Z. Physik B. 65, (4 ), 409 –413 (1987). 0722-3277 CrossRef
Pine  D. J. et al., “Diffusing wave spectroscopy,” Phys. Rev. Lett.. 60, (12 ), 1134 –1137 (1988). 0031-9007 CrossRef
Diop  M. et al., “Calibration of diffuse correlation spectroscopy with a time-resolved near-infrared technique to yield absolute cerebral blood flow measurements,” Biomed. Opt. Exp.. 2, (7 ), 2068 –2081 (2011). 2156-7085 CrossRef
Roche-Labarbe  N. et al., “Noninvasive optical measures of CBV, StO2, CBF index, and rCMRO2 in human premature neonates’ brains in the first six weeks of life,” Hum. Brain Mapp.. 31, (3 ), 341 –352 (2010). 1065-9471 CrossRef
Jain  V. et al., “Cerebral oxygen metabolism in neonates with congenital heart disease quantified by MRI and optics,” J. Cereb. Blood Flow Metab.. 34, (3 ), 380 –388 (2014). 0271-678X CrossRef
Verdecchia  K. et al., “Quantifying the cerebral metabolic rate of oxygen by combining diffuse correlation spectroscopy and time-resolved near-infrared spectroscopy,” J. Biomed. Opt.. 18, (2 ), 027007  (2013). 1083-3668 CrossRef
Zhou  C. et al., “Diffuse optical monitoring of hemodynamics in piglet brain with head trauma injury,” J. Biomed. Opt.. 14, (3 ), 034015  (2009). 1083-3668 CrossRef
Durduran  T. et al., “Spatiotemporal quantification of cerebral blood flow during functional activation in rat somatosensory cortex using laser-speckle flowmetry,” J. Cereb. Blood Flow Metab.. 24, (5 ), 518 –525 (2004). 0271-678X CrossRef
Fercher  A. F., Briers  J. D., “Flow visualization by means of single-exposure speckle photography,” Opt. Commun.. 37, (5 ), 326 –330 (1981). 0030-4018 CrossRef
Parthasarathy  A. B., “Laser speckle contrast imaging of cerebral blood flow in humans during neurosurgery: a pilot clinical study,” J. Biomed. Opt.. 15, (6 ), 066030  (2010). 1083-3668 CrossRef
Kazmi  S. M. et al., “Chronic imaging of cortical blood flow using multi-exposure speckle imaging,” J. Cereb. Blood Flow Metab.. 33, (6 ), 798 –808 (2013). 0271-678X CrossRef
Boas  D. A., Dunn  A. K., “Laser speckle contrast imaging in biomedical optics,” J. Biomed. Opt.. 15, (1 ), 011109  (2010). 1083-3668 CrossRef
Dunn  A. K., “Dynamic imaging of cerebral blood flow using laser speckle,” J. Cereb. Blood Flow Metab.. 21, (3 ), 195 –201 (2001). 0271-678X CrossRef
Rice  T. B. et al., “Quantitative determination of dynamical properties using coherent spatial frequency domain imaging,” J. Opt. Soc. Am. A.. 28, (10 ), 2108 –2114 (2011). 0740-3232 CrossRef
Mazhar  A. et al., “Laser speckle imaging in the spatial frequency domain,” Biomed. Opt. Exp.. 2, (6 ), 1553 –1563 (2011). 2156-7085 CrossRef
Bi  R., Dong  J., Lee  K., “Deep tissue flowmetry based on diffuse speckle contrast analysis,” Opt. Lett.. 38, (9 ), 1401 –1403 (2013). 0146-9592 CrossRef
Gagnon  L., “Investigation of diffuse correlation spectroscopy in multi-layered media including the human head,” Opt. Exp.. 16, (20 ), 15514 –15530 (2008). 1094-4087 CrossRef
Mesquita  R. et al., “Influence of probe pressure on the diffuse correlation spectroscopy blood flow signal: extra-cerebral contributions,” Biomed. Opt. Exp.. 4, (7 ), 978 –994 (2013). 2156-7085 CrossRef
Irwin  D. et al., “Influences of tissue absorption and scattering on diffuse correlation spectroscopy blood flow measurements,” Biomed. Opt. Exp.. 2, (7 ), 1969 –1985 (2011). 2156-7085 CrossRef
Dietsche  G. et al., “Fiber-based multispeckle detection for time-resolved diffusing-wave spectroscopy: characterization and application to blood flow detection in deep tissue,” Appl. Opt.. 46, (35 ), 8506 –8514 (2007). 0003-6935 CrossRef
He  L. et al., “Using optical fibers with different modes to improve the signal-to-noise ratio of diffuse correlation spectroscopy flow-oximeter measurements,” J. Biomed. Opt.. 18, (3 ), 037001  (2013). 1083-3668 CrossRef
Lin  P. et al., “Non-invasive optical measurement of cerebral metabolism and hemodynamics in infants,” J. Visual. Exp.. 14, (73 ), e4379  (2013). 1940-087X CrossRef
Yucel  M. A. et al., “Targeted principle component analysis: a new motion artifact correction approach for near-infrared spectroscopy,” J. Innovative Opt. Health Sci.. 7, (2 ), 1350066  (2014). 1793-7205 CrossRef
Cooper  R. J. et al., “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci.. 6, (147 ) (2012). 0887-3658 CrossRef
Yucel  M. A. et al., “Reducing motion artifacts for long-term clinical NIRS monitoring using collodion-fixed prism-based optical fibers,” Neuroimage. 85, (Pt 1 ), 192 –201 (2014). 1053-8119 CrossRef
Shang  Y. et al., “Effects of muscle fiber motion on diffuse correlation spectroscopy blood flow measurements during exercise,” Biomed. Opt. Exp.. 1, (2 ), 500 –511 (2010). 2156-7085 CrossRef
Gurley  K., Shang  Y., Yu  G., “Noninvasive optical quantification of absolute blood flow, blood oxygenation, and oxygen consumption rate in exercising skeletal muscle,” J. Biomed. Opt.. 17, (7 ), 075010  (2012). 1083-3668 CrossRef
Carp  S. et al., “Validation of diffuse correlation spectroscopy measurements of rodent cerebral blood flow with simultaneous arterial spin labeling MRI; towards MRI-optical continuous cerebral metabolic monitoring,” Biomed. Opt. Exp.. 1, (2 ), 553 –565 (2010). 2156-7085 CrossRef
Cheung  C. et al., “In vivo cerebrovascular measurement combining diffuse near-infrared absorption and correlation spectroscopies,” Phys. Med. Biol.. 46, (8 ), 2053 –2065 (2001). 0031-9155 CrossRef
Culver  J. P. et al., “Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia,” J. Cereb. Blood Flow Metab.. 23, (8 ), 911 –924 (2003). 0271-678X CrossRef
Shang  Y. et al., “Diffuse optical monitoring of repeated cerebral ischemia in mice,” Opt. Exp.. 19, (21 ), 20301 –20315 (2011). 1094-4087 CrossRef
Zhou  C. et al., “Diffuse optical correlation tomography of cerebral blood flow during cortical spreading depression in rat brain,” Opt. Exp.. 14, (3 ), 1125 –1144 (2006). 1094-4087 CrossRef
Franceschini  M. A. et al., “The effect of different anesthetics on neurovascular coupling,” Neuroimage. 51, , 1367 –1377 (2010). 1053-8119 CrossRef
Durduran  T. et al., “Transcranial optical monitoring of cerebrovascular hemodynamics in acute stroke patients,” Opt. Exp.. 17, (5 ), 3884 –3902 (2009). 1094-4087 CrossRef
Kim  M. N. et al., “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocrit. Care. 12, (2 ), 173 –180 (2010). 1541-6933 CrossRef
Kim  M. N. et al., “Continuous optical monitoring of cerebral hemodynamics during head-of-bed manipulation in brain-injured adults,” Neurocrit. Care. (2013). 1541-6933 CrossRef
Mesquita  R. C. et al., “Optical monitoring and detection of spinal cord ischemia,” PLoS One. 8, (12 ), e83370  (2013). 1932-6203 CrossRef
Tellis  G. M., Mesquita  R. C., Yodh  A. G., “Use of diffuse correlation spectroscopy to measure brain blood flow differences during speaking and nonspeaking tasks for fluent speakers and persons who stutter,” SIG 4 Perspect. Fluency Fluency Disord.. 21, , 96 –106 (2011). 1940-7599 CrossRef
Edlow  B. L. et al., “The effects of healthy aging on cerebral hemodynamic responses to posture change,” Physiol. Meas.. 31, (4 ), 477 –495 (2010). 0967-3334 CrossRef
Mesquita  R. C. et al., “Blood flow and oxygenation changes due to low-frequency repetitive transcranial magnetic stimulation of the cerebral cortex,” J. Biomed. Opt.. 18, (6 ), 067006  (2013). 1083-3668 CrossRef
Durduran  T. et al., “Diffuse optical measurement of blood flow, blood oxygenation, and metabolism in a human brain during sensorimotor cortex activation,” Opt. Lett.. 29, (15 ), 1766 –1768 (2004). 0146-9592 CrossRef
Jaillon  F. et al., “Activity of the human visual cortex measured non-invasively by diffusing-wave spectroscopy,” Opt. Exp.. 15, , 6643 –6650 (2007). 1094-4087 CrossRef
Li  J. et al., “Transient functional blood flow change in the human brain measured noninvasively by diffusing-wave spectroscopy,” Opt. Lett.. 33, (19 ), 2233 –2235 (2008). 0146-9592 CrossRef
Durduran  T. et al., “Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects,” J. Biomed. Opt.. 15, (3 ), 037004  (2010). 1083-3668 CrossRef
Buckley  E. M. et al., “Cerebral hemodynamics in preterm infants during positional intervention measured with diffuse correlation spectroscopy and transcranial Doppler ultrasound,” Opt. Exp.. 17, (15 ), 12571 –12581 (2009). 1094-4087 CrossRef
Buckley  E. M. et al., “Sodium bicarbonate causes dose-dependent increases in cerebral blood flow in infants and children with single ventricle physiology,” Pediatr. Res.. 73, (5 ), 668 –673 (2013). 0031-3998 CrossRef
Buckley  E. M. et al., “Early postoperative changes in cerebral oxygen metabolism following neonatal cardiac surgery: effects of surgical duration,” J. Thorac. Cardiovasc. Surg.. 145, (1 ), 196 –203 (2013). 0022-5223 CrossRef
Dehaes  M. et al., “Cerebral oxygen metabolism in neonatal hypoxic ischemic encephalopathy during and after therapeutic hypothermia,” J. Cereb. Blood Flow Metab.. 34, (1 ), 87 –94 (2014). 0271-678X CrossRef
Roche-Labarbe  N. et al., “Somatosensory evoked changes in cerebral oxygen consumption measured non-invasively in premature neonates,” Neuroimage. 85, (Pt 1 ), 279 –286 (2014). 1053-8119 CrossRef
Roche-Labarbe  N. et al., “Near-infrared spectroscopy assessment of cerebral oxygen metabolism in the developing premature brain,” J. Cereb. Blood Flow Metab.. 32, (3 ), 481 –488 (2012). 0271-678X CrossRef
Lin  P.-Y. et al., “Regional and hemispheric asymmetries of cerebral hemodynamic and oxygen metabolism in newborns,” Cereb. Cortex. 23, (2 ), 339 –348 (2013). 1047-3211 CrossRef
Boas  D. A., Franceschini  M. A., “Haemoglobin oxygen saturation as a biomarker: the problem and a solution,” Philos. Transact. A, Math. Phys. Eng. Sci.. 369, (1995 ), 4407 –4424 (2011).CrossRef

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Prosodic grouping at birth. Brain Lang 2016;162():46-59.
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.