2.1.

Light Attenuation Coefficient and Depth Penetration

Absorption in biological tissues is dominated by hemoglobin in the ultraviolet and visible spectral regions (200 to 650 nm) and by water for wavelengths above 900 nm.²⁸ Between 650 and 900 nm, absorption is more than one order of magnitude lower, and NIRS uses this transmission window to investigate tissue several cm below the surface. Between 1050 and 1100 nm, the water spectrum has a local minimum [Fig. 1(a)], offering an additional transmission window for deep tissue measurements. Above 1100 nm, water absorption continues to increase with peaks and minima well above the 1050 to 1100 nm values. Figure 1(a) shows the absorption spectra of the main tissue chromophores between 600 and 1200 nm using assumptions typical for the brain. For the hemoglobin absorption, we assumed a concentration (HbT) of $80\mu \mathrm{M}$, an oxygenation (${\mathrm{SO}}_2$) of 62.5%, and used extinction coefficients compiled by Prahl et al.²⁹ as well as by Bosschaart et al.³⁰ For the water absorption, we used values from Hale et al.³¹ and assumed a 75% volume fraction in tissue.³² For fat absorption, we used values from van Veen et al.³³ and assumed a 20% volume fraction.³⁴ Under these assumptions, the total absorption coefficients (${\mu }_a$) at 765, 785, and 850 nm, wavelengths commonly used for DCS, are 0.19, 0.17, and $0.20{\mathrm{cm}}^{-1}$, respectively, very similar to the total absorption at 1064 nm ($0.18{\mathrm{cm}}^{-1}$). By varying the hemoglobin concentration between 40 and $120\mu \mathrm{M}$, hemoglobin oxygenation between 40% and 95%, fat content between 0% and 40%, and water content between 55% and 95%, on average, we obtain absorption coefficients equal to the one in our example (see Table 1).

Fig. 1

(a) Absorption spectra of main chromophores in brain tissue between 600 and 1200 nm assuming $\mathrm{HbT}=80\mu \mathrm{M}$, ${\mathrm{SO}}_2=62.5\%$, 20% volume fraction of fat and 75% volume fraction of water: oxy-hemoglobin (HbO), red squares; deoxy-hemoglobin (HbR), magenta stars; water, blue diamonds; and fat, green crosses. The thick black line is the resulting total absorption. Absorption of melanin, cytochrome oxidase, and other possible chromophores is below ${10}^{-3}{\mathrm{cm}}^{-1}$ in most biological tissues. For shorter wavelengths, hemoglobin absorption increases; for longer wavelengths, water absorption further increases. (b) Reduced scattering coefficient assuming a power law wavelength dependence with $a=20$ and $b=1.5$ for ${\lambda }_0=500\mathrm{nm}$, thick black line, and changing $a$ and $b$ within physiological ranges, colored lines with symbols. (c) Resulting effective attenuation coefficient: thick black line attenuation is derived from the total absorption of (a) and reduced scattering coefficient with $a=20$ and $b=1.5$ for ${\lambda }_0=500\mathrm{nm}$; for red squares and magenta stars, we assumed $b=1$ and = 2, respectively; blue diamonds for low hemoglobin, assuming $\mathrm{HbT}=40\mu \mathrm{M}$ and ${\mathrm{SO}}_2=50\%$; green crosses for high hemoglobin, assuming $\mathrm{HbT}=150\mu \mathrm{M}$ and ${\mathrm{SO}}_2=75\%$.

Table 1

Mean and standard deviation of optical properties across the range of chromophore concentrations considered in simulation.

In biological tissues, the reduced scattering coefficient (${\mu }_s^{\prime }$) decreases with wavelength and follows the empirical power law relationship of ${\mu }_s^{\prime }(\lambda )=a{(\lambda /{\lambda }_0)}^{-b}$, where $a$ is a scaling factor at wavelength ${\lambda }_0$ and $b$ is the scattering power. The scattering spectra for typical values of $a=20$ at ${\lambda }_0=500\mathrm{nm}$ and $b=1.5$ (black line)^28,33 are shown in Fig. 1(b), together with additional example spectra assuming $a=15$, $a=25$, $b=1$, or $b=2$. Similarly, we varied the scattering parameters over a wide range of $8<a<25$ and $0.5<b<2.4$ and calculated that on average at 1064 nm ${\mu }_s^{\prime }$ is 35%, 32%, and 25% lower than at 765, 785, and 850 nm, respectively (Table 1).

The cumulative effect of absorption and scattering on light propagation in the diffusive regime is given by the effective attenuation coefficient [${\mu }_{\mathrm{eff}}(\lambda )=\sqrt{3{\mu }_a({\mu }_a+{\mu }_s^{\prime })}$]. Figure 1(c) reports ${\mu }_{\mathrm{eff}}$ spectra for typical absorption and scattering [black lines in (a)–(c)], for high- and low-scattering power ($b=1$ red squares and $b=2$ magenta stars), and for low and high hemoglobin concentration ($40\mu \mathrm{M}$: blue diamonds and $150\mu \mathrm{M}$: green crosses). Over the wide ranges of absorption and scattering described above, the average effective attenuation coefficient for diffuse light at 1064 nm is 23%, 21%, and 21% lower than at 765, 785, and 850 nm, respectively (Table 1).

In the examples of Fig. 1, we used a high content of water (75%), typical of brain tissue.³² For biological tissues with lower water content, the reduction in the effective attenuation at 1064 nm is even larger, despite the increase in fat content, which has a negligible contribution.

Figure 2 presents histograms of the optical absorption, reduced scattering, and effective attenuation coefficients at 765, 785, 850, and 1064 nm, respectively, across 150,000 randomly sampled scenarios from the tissue chromophore concentration and scattering parameter ranges given above. Of note, when compared in a pairwise fashion across wavelengths for each randomly chosen chromophore concentration and scattering parameter choice set, the effective attenuation coefficient was lower at 1064 nm than at 765, 785, and 850 nm in 96%, 93%, and 99% of the time, respectively.

Fig. 2

Histograms of absorption, reduced scattering, and effective attenuation coefficients at 765, 785, 850, and 1064 nm, respectively, randomly sampled ($N=\mathrm{150,000}$) for $40\mu \mathrm{M}<\mathrm{HbT}<120\mu \mathrm{M}$, $40\%<{\mathrm{SO}}_2<85\%$, $0.55<f_{\mathrm{H}2\mathrm{O}}<0.95$, $0<f_{\text{fat}}<0.4$, $8<a<25$, $0.5<b<2.4$ to cover the vast majority of possible circumstances for tissue measurements. Vertical bars indicate average values. In pairwise comparisons (across the set of randomly sampled parameters), ${\mu }_{\mathrm{eff},1064}$ was lower than ${\mu }_{\mathrm{eff},765}$, ${\mu }_{\mathrm{eff},785}$, and ${\mu }_{\mathrm{eff},850}$ in 96%, 93%, and 99% of the time, respectively, indicating 1064 nm nearly always offers lower attenuation than the shorter wavelengths commonly used for DCS measurements.

A lower effective attenuation coefficient implies a larger number of photons traveling through the tissue will be able to reach the detector. In fact, in the diffusive regime, the relationship between the detected intensity at two wavelengths is

The fact that hemoglobin absorption is low at 1064 nm makes this wavelength choice impractical for NIRS, but this is not an issue for DCS as DCS contrast arises from the dynamic scattering of light by moving red blood cells, which is still high at 1064 nm. The lower scattering at 1064 nm does result in a somewhat reduced sensitivity to motion; however, this is more than compensated for by the other favorable aspects of using 1064 nm operation, as demonstrated below.

2.2.

Overall Photon Budget Considerations

In addition to the lower attenuation, other factors contribute to the larger number of detectable photons at 1064 nm than at conventional DCS wavelengths.

DCS, like NIRS, needs to be performed in compliance with the ANSI standards limits for safe skin exposure (ANSI Z136.1) to be categorized as a nonsignificant risk device; thus, it needs to irradiate the tissue with low-power light. Based on these standards, the maximum permissible exposure (MPE) of skin by optical illumination are ${\mathrm{MPE}}_{765}=0.27\mathrm{W}/{\mathrm{cm}}^2$, ${\mathrm{MPE}}_{785}=0.29\mathrm{W}/{\mathrm{cm}}^2$, ${\mathrm{MPE}}_{850}=0.40\mathrm{W}/{\mathrm{cm}}^2$, while from 1000 to 1400 nm ${\mathrm{MPE}}_{1000-1400}=1.07\mathrm{W}/{\mathrm{cm}}^2$. Per the standard, for an illumination spot larger than 1-mm-diameter, a 3.5-mm aperture can be applied, translating to a light power of 26 mW at 765 nm, 28 mW at 785 nm, 38 mW at 850 nm, and 103 mW at 1064 nm. This means that at 1064 nm we can deliver 2.7 to 4 times more energy than at wavelengths between 765 and 850 nm. Moreover, because photons at longer wavelengths carry less energy $E_{{\lambda }_2}=E_{{\lambda }_1}·\frac{{\lambda }_1}{{\lambda }_2}$, at longer wavelengths this increases the number of photons per unit of energy. Hence at 1064 nm, we can emit 1.3 to 1.4 times more photons per unit of illumination power than at wavelengths between 765 and 850 nm.

Multiplying together these three factors, i.e., the lower effective attenuation, the higher MPE, and the larger number of photons per unit of energy, using a 3-cm source–detector separation at 1064 nm, we end up with 13, 10.5, and 7 times more photons at the detector than at 765, 785, and 850 nm, respectively. This is a considerable gain as the number of photons directly impacts the measurement SNR.

2.3.

Intensity Autocorrelation Noise Considerations

Figure 3(a) shows example $g_2$ curves for a 3-cm source–detector separation, using the optical properties in Table 1 and assuming a blood flow index, ${\mathrm{BF}}_i=2\times {10}^{-8}{\mathrm{cm}}^2/\mathrm{s}$. The lower scattering and longer wavelength result in a later autocorrelation decay at 1064 nm versus shorter wavelengths, moving the correlation transition region to longer delay time bins where the statistical fluctuation noise is lower at a given photon count. Figure 3(b) shows updated $g_2$ simulations in which all factors discussed so far are taken into account to generate curves with realistic noise based on the DCS correlation noise model from Zhou.³⁵ To this end actual photon counts needed to be specified, which in fact is one of the major advantages of operating at 1064 nm. Hence, we assumed a photon count rate of 7 kcps through a single-mode fiber for the 765-nm measurement, estimated from our experience with in vivo measurements. We simulated measuring with four co-located detector fibers to increase SNR³⁶—as is often done in practice. This photon count rate was scaled up conservatively by just half of the factors estimated in Sec. 2.2 at the other wavelengths and considered 8050 counts per second at 785 nm, 10,900 counts per second at 850 nm, and 42,500 counts per second at 1064 nm. A 10-s averaging time was assumed. It is apparent from this simulation that, even under these conservative assumptions, 1064 nm measurements benefit from a substantial increase in SNR.

Fig. 3

Simulated $g_2$ curves for measurements with 3-cm source–detector separation on a homogeneous sample with optical properties matching Table 1, assuming $\beta =0.5$, an integration time of 10 s, and a blood flow index ${\mathrm{BF}}_i=2\times {10}^{-8}{\mathrm{cm}}^2/\mathrm{s}$: (a) noise free; (b) with realistic noise added assuming a 7-kcps count rate at 765 nm, 8.05 kcps at 785 nm, 10.9 kcps at 850 nm, and 42.5 kcps 1064 nm on each of 4 co-located fibers.

2.4.

Monte Carlo Simulations of Sensitivity to Deep Blood Flow Change in the Presence of Realistic Noise

To further give a realistic assessment of the expected performance of DCS at 1064 nm in practice, we used Monte Carlo simulations in conjunction with a correlation noise model³⁵ and typical experimental parameters to estimate the level of change in cerebral blood flow that can be detected with 95% confidence and 80% power. To this end, we considered a two-layered slab, with a superficial layer thickness of 1 cm and ran separate simulations corresponding to 765, 850, and 1064 nm illumination, respectively. For the two layers, we assumed typical hemoglobin and water concentrations as reported in the literature.³⁷ Specifically, we assumed that the superficial layer contains $30\mu \mathrm{M}$ HbT, with a 66% ${\mathrm{SO}}_2$ and has a water fraction of 0.6. We assumed that the deep (cerebral) layer contains $80\mu \mathrm{M}$ HbT with a 62.5% ${\mathrm{SO}}_2$ and has a water fraction of 0.75 (as in Fig. 1). Optical absorption was derived from these chromophore concentrations using published extinction coefficients at each wavelength as detailed in the previous sections. Optical scattering was assumed to be homogeneous, equal to 9, 8, and $6{\mathrm{cm}}^{-1}$, respectively, at the three wavelengths, following the average optical scattering derived in the previous section (as shown in Table 1). A blood flow index of $1\times {10}^{-8}{\mathrm{cm}}^2/\mathrm{s}$ was assumed for the superficial layer, and a blood flow index of $6\times {10}^{-8}{\mathrm{cm}}^2/\mathrm{s}$ was assumed for the deep layer, similar to our previous publication.²² To assess sensitivity to changes in blood flow in the deeper layer, the deep flow was increased in turn by amounts from 1% to 100% in steps of 1%. We fixed the source–detector separation to 3 cm, the largest source–detector separation commonly used for in vivo measurements with existing DCS systems.

Electric field autocorrelation functions $g_1=⟨E(t)E^{*}(t+\tau )⟩/⟨E(t)E^{*}(t)⟩$ were derived from photon pathlengths and momentum transfer accumulations in each tissue type using the MCX simulation package.³⁸ $g_1$ curves were then converted to intensity autocorrelation $g_2$ curves using the Siegert relationship $g_2=1+\beta$. $g_1^2$ with $\beta =0.5$ corresponding to polarization-insensitive detection.

The Monte Carlo generated $g_2$ curves were further modified to add realistic noise levels based on the model of Zhou.³⁵ Hence, taking into account the photon budget considerations discussed in Sec. 2.2, we used the same fluence values as used in Sec. 2.3 and assumed four co-located fibers as well. The noise model predicted standard deviations for $g_2$ at each lag time were applied by randomly sampling a normal distribution. A total of 60 noise realizations were obtained at each flow level, and the integration time in the noise model was set to 1 s, i.e., we simulated 60 1-s measurements at each flow level.

To generate population-based statistics, each sequence of 60 “measurements” (at baseline or at elevated deep flow levels, respectively) was divided into six 10-s segments. Each baseline segment was combined with each elevated flow segment at each flow level providing a population of 36 “trials” at each deep flow change level. Thus each trial was an attempt to detect a statistically significant change ($p<0.05$) in deep flow by comparing 10 s of baseline data with 10 s of elevated flow data, both acquired with a 1-s integration time. We consider this to be a realistic scenario for neuromonitoring in clinical practice, for example.

Figure 4 shows the results of the simulations. DCS at 765 nm (red diamonds) is able to detect significant changes in blood flow in, at best, 30% to 40% of the trials even for the highest level of simulated deep blood flow changes (above 80% with respect to baseline). DCS at 850 nm (blue squares) performances are somewhat better, though deep flow changes above 75% are needed to achieve an 80% detection rate of deep flow change. By comparison, the performance of DCS at 1064 nm (green circles) is dramatically better with more than 80% of the trials able to detect a statistically significant change in deep blood flow of 20% or more.

Fig. 4

Probability of detecting a statistically significant change in deep-tissue blood flow from a pair of 10 s measurements as a function of the actual amount of change for noise-realistic simulated measurements at 765 nm (red diamonds), 850 nm (blue squares), and 1064 nm (green circles). We assumed $30\mu \mathrm{M}$ HbT, with a 66% ${\mathrm{SO}}_2$, water fraction of 0.6 (no fat), and ${\mathrm{BF}}_i={10}^{-8}{\mathrm{cm}}^2/\mathrm{s}$ for the superficial layer (1-cm thick), and $80\mu \mathrm{M}$ HbT with a 62.5% ${\mathrm{SO}}_2$, water fraction of 0.75 (also with no fat), and ${\mathrm{BF}}_i=6\times {10}^{-8}{\mathrm{cm}}^2/\mathrm{s}$ for the deep layer. The source–detector separation was set to 3 cm, and we assumed 7 kcps at 765 nm on four co-located fibers, scaling up to 10.9 and 42.5 kcps at 850 and 1064 nm, respectively, reflecting conservative estimates of the benefits of longer wavelengths.

3.1.

DCS Hardware at 1064 nm

3.2.

Phantom Measurements

Figure 5 shows the CW-DCS temporal autocorrelation functions for 3-cm source–detector separation measured at 765 nm (red diamonds) and 1064 nm (green circles) in (a) silicone oil and (b) intralipid. The decay of $g_2$ is much slower (occurs at later time lags) for the two homogeneous liquid phantoms at 1064 nm because of the lower scattering and higher wavelength. If we calculate ${\mathrm{BF}}_i$ assuming reduced scattering values of $8{\mathrm{cm}}^{-1}$ at 765 nm and $6{\mathrm{cm}}^{-1}$ at 1064 nm, we obtain the same ${\mathrm{BF}}_i$ in the two phantoms.

Fig. 5

Intensity autocorrelation function ($g_2$) measurements at 765 nm (red diamonds) and 1064 nm (green circles) in (a) a silicone oil phantom and (b) an intralipid phantom.

Another set of liquid phantom titrations was used to further verify that the same dynamic properties are recovered no matter what wavelength is used to perform the measurements. The phantoms were constructed by adding India ink to a diluted intralipid suspension in water. Optical properties at 765 and 850 nm were measured using a frequency-domain NIRS device (ISS MetaOx). Optical properties at 1064 nm were extrapolated from measurements in the 660- to 830-nm range. As shown in Fig. 6, using CW-DCS at the three wavelengths, we were able to obtain agreement of 10% or better in the measured Brownian diffusion coefficient values across wavelengths when taking into account the actual optical properties. Of note, due to water absorption, significant amounts of ink needed to be added to obtain comparable absorption at 765 and 850 nm compared with 1064 nm.

Fig. 6

CW-DCS measurements of ${\mathrm{BF}}_i$ (in this case, the Brownian diffusion coefficient $D_b$) in a liquid phantom across wavelengths and phantom optical properties. (a) Optical absorption values, (b) corresponding optical scattering values, (c) corresponding attenuation coefficient values, and (d) recovered ${\mathrm{BF}}_i$ taking into account actual optical properties.

3.3.

In Vivo Measurements

We conducted several proof-of-concept experiments to demonstrate the feasibility of in vivo DCS data acquisition at 1064 nm and to exemplify the advantages of operating at this wavelength. Subject consent was obtained in accordance with the policies and guidelines of the Massachusetts General Hospital/Partners Healthcare Institutional Review Board.

3.3.1.

Muscle blood flow during arm cuff occlusion

As a first demonstration, we conducted an arm cuff occlusion measurement on three healthy volunteers using both 765 nm and 1064 nm CW-DCS systems. These tests were conducted using the Excelitas SPADs; thus, we chose a 1-cm source–detector separation to compensate for the low PDE at 1064 nm. Figure 7 shows the time courses of the relative changes in blood flow index measured at 765 and 1064 nm, normalized to the average of the first 20 s for the three different subjects. After 40 s of baseline, the blood pressure cuff was quickly inflated to 160 mmHg in each case. The pressure was then released about 55 s later. Two measurements were conducted in succession on each subject at 765 and 1064 nm, respectively. Scattering values of $8{\mathrm{cm}}^{-1}$ at 765 nm and $6{\mathrm{cm}}^{-1}$ at 1064 nm were chosen to obtain matching absolute blood flow index values at the baseline. Although the degree of blood flow reduction during occlusion and of reactive hyperemia when the pressure was released varied by subject, the DCS measurements at the two wavelengths agreed very well with each other, despite being conducted sequentially.

Fig. 7

Measurements of muscle blood flow before, during, and after an arm cuff occlusion maneuver in three different subjects. Measurements at 765 nm (red diamonds) and 1064 nm (green circles) were conducted one after the other. Despite the nonsimultaneous nature of the measurement, there is very good agreement between measurements taken in the same subject.

3.3.2.

Cerebral blood flow measurements

Measurements were obtained using both the CW-DCS and TD-DCS systems on the forehead of an adult volunteer. The 765-nm (or 785 nm) measurements used our standard systems,^39,40 and we delivered 26 mW (28 mW at 785 nm) of average power on the skin surface. The 1064-nm measurements were conducted using the TOPTICA eagleyard single-frequency laser diode for CW-DCS and the Picoquant pulsed seed laser for TD-DCS, both amplified to 90 mW of average power using the Cybel MantaRay fiber amplifier. Either Photon Spot or Quantum Opus SNSPD detectors were used for both the CW-DCS and TD-DCS measurements at 1064 nm.

Figure 8 shows the CW-DCS temporal autocorrelation functions for a 3-cm source–detector separation measured at 765 nm (red diamonds) and 1064 nm (green circles) on an adult human forehead. The difference between the $g_2\mathrm{s}$ at the two wavelengths is smaller than in the phantom measurements shown in Fig. 5 (though remaining in favor of the 1064-nm measurement) because at 1064 nm light penetrates deeper and sees more of the brain blood flow. The higher sensitivity to the high perfusion in the brain “accelerates” the 1064-nm $g_2$, resulting in a more similar decay of $g_2$ at the two wavelengths. The 1064-nm measurement retains a significant SNR advantage due to the higher photon count ($\sim 55\mathrm{kcps}$ at 1064 nm versus 7 kcps at 765 nm in this case, driven by higher photon availability and higher PDE of the SNSPD detector even with more brain tissue in the photon path). If we calculate ${\mathrm{BF}}_i$ assuming values of $8{\mathrm{cm}}^{-1}$ at 765 nm and $6{\mathrm{cm}}^{-1}$ at 1064 nm, we obtain a ${\mathrm{BF}}_i$ that is 50% higher at 1064 nm than at 765 nm in the forehead.

Fig. 8

$g_2$ measurements at 765 nm (red diamonds) and 1064 nm (green circles) on the forehead of a human subject. The 1064-nm measurements show a later decay and significantly higher SNR.

Figure 9 shows the time course of CW-DCS ${\mathrm{BF}}_i$ at a 10-Hz acquisition rate, during a probe pressure modulation maneuver (marked by the gray shading starting just after the 60-s mark). By lightly pressing the optical probe onto the forehead, we reduce the blood flow to the skin under the probe. This results in a lower ${\mathrm{BF}}_i$ in the scalp and an unaffected ${\mathrm{BF}}_i$ in the brain. At a short source–detector separation (5 mm, orange/light green curves with open circles, respectively), we only measure ${\mathrm{BF}}_i$ in the scalp and the pressure modulation decreases ${\mathrm{BF}}_i$ substantially. At the same time, pulsatility is largely reduced. At a 3-cm separation (red/dark green curves with filled circles, respectively), we see a smaller reduction in average flow and pulsatility is preserved. Although evidence of cardiac cycle ${\mathrm{BF}}_i$ variation is visible at both wavelengths, the SNR of the 785-nm measurement is insufficient to resolve the pulsation, and the long-separation fit is rather unstable. At the same time, at 1064 nm we observe excellent SNR for the ${\mathrm{BF}}_i$ time course, easily resolving the pulsation, including finer features such as the dichroitic notch. These data demonstrate the ability to use 1064 nm DCS as a functional imaging technique with high temporal resolution.

Fig. 9

CW-DCS ${\mathrm{BF}}_i$ time course measurements during pressure modulation maneuvers on the forehead of a human subject using a 10 Hz acquisition rate at (a) 785 nm (orange/red symbols) and (b) 1064 nm (light/dark green symbols), respectively. The short separation (5 mm: orange/light green open circles, respectively) ${\mathrm{BF}}_i$ decreases as expected during the pressure period and is no longer pulsatile; cardiac pulsation remains apparent in the long separation channel (30 mm: red/dark green filled circles, respectively) at both wavelengths; however, only 1064 nm offers sufficient SNR to resolve the pulsation at a 3-cm source–detector separation.

Figure 10 exemplifies TD-DCS measurements in the human forehead at 765 and 1064 nm using a 1-cm source–detector separation and employing the maximum power allowed by the regulatory standards. SNSPDs were used as the detector for both wavelengths. The data were processed at 5 Hz, after which the ${\mathrm{BF}}_i$ values were downsampled to $1/3\mathrm{Hz}$ to filter out cardiac pulsation and compute the coefficient of variation (CoV). The resulting ${\mathrm{BF}}_i$ CoVs versus gate start time for gates 150 ps wide are shown in Fig. 10(a) for 765 and 1064 nm. Although the CoV increases quicky versus gate at 765 nm and the fit becomes unstable by 600 ps after the peak of the temporal point spread function (TPSF), the 1064-nm curve remains relatively stable until 800 ps after the peak of the TPSF (likely dominated by physiological variability), and the CoV only shows a significant increase at 1 ns after the peak of the TPSF. Figure 10(b) shows the original 5 Hz data for 1064 nm TD-DCS measurements for an early gate (starting at the peak of the TPSF) and a late gate (starting 1 ns after the peak of the TPSF). The ${\mathrm{BF}}_i$ time traces clearly offer sufficient SNR to resolve the cardiac pulsation even at the late gate, exceeding any previously demonstrated TD-DCS measurements with sub 1 s integration times. The achieved SNR highlights the strong potential of TD-DCS as tool for functional brain imaging in addition to neuromonitoring applications.

Fig. 10

TD-DCS comparison between 765 and 1064 nm. (a) Comparison of the ${\mathrm{BF}}_i$ CoV versus gate start time for TD-DCS measurements conducted at 765 nm (red diamonds) and 1064 nm (green circles) using a 1-cm source–detector separation and the maximum power allowed by regulatory standards. (b) Evidence of cardiac pulsation in ${\mathrm{BF}}_i$ at a 5-Hz acquisition rate for early (0 to 150 ps, light green filled circles) and late (1000 to 1150 ps, dark green open circles) gates.