Currently, transcranial Doppler ultrasound (TCD) and near-infrared spectroscopy (NIRS) are the only techniques deemed feasible for the estimation of CBF in this clinical population. TCD measures cerebral blood flow velocity (CBFV) in the cerebral arteries by monitoring the frequency shift of acoustic waves that scatter from moving red blood cells [7,8]. With additional information about the cross-sectional area of the insonated vessel, CBFV permits calculations of arterial CBF. However, cerebral vessels are small in size, making their diameter difficult to measure [9]. To avoid this source of error, one could focus on relative changes in flow. However, these blood vessels can change caliber over time, leading to large errors in calculations of relative change, which in turn cause errors in estimates of the amount of oxygen and nutrients delivered to the surrounding tissue [10–12].

Near-infrared spectroscopy measures tissue oxy- and deoxyhemoglobin concentrations, taking advantage of the tissue absorption “window” in the near-infrared [13]. A comprehensive review of near-infrared spectroscopy in the neonate was recently published by Wolfberg and du Plessis [14]. Although NIRS measurements of tissue oxygenation and total hemoglobin concentration are increasingly more common in the clinic, the calculation of CBF from NIRS data is indirect and relies on the Fick principle, which states that the total uptake of a tracer by tissue is proportional to the difference between the rates of inflow and outflow of the tracer to and from the tissue [15]. This calculation requires the use of a tracer, typically oxygen or indocyanine green, and requires certain assumptions to be valid, namely that cerebral blood volume, CBF, and cerebral oxygen extraction must remain constant.

In this paper we employ another recently developed optical technique to measure CBF: diffuse correlation spectroscopy (DCS). DCS [16–21] has shown promise as a monitor of relative changes in blood flow [21–35]. Like NIRS, DCS also employs near-infrared light to probe the dynamics of deep tissues. However, DCS detects changes in CBF directly by monitoring temporal fluctuations of scattered light. It does not rely on tracers to indirectly infer information about CBF, and it can be employed continuously. Finally, in contrast to TCD, DCS provides information about microvascular hemodynamics.

In the present investigation, diffuse correlation spectroscopy and transcranial Doppler ultrasound monitor the hemodynamics of four very low birthweight, very preterm neonates during a 0° to 12° postural change. Because this study is a feasibility test, we limited head of bed elevation to values within the range of the clinical isolette beds. Many studies have been conducted to determine the physiological response of preterm infants to postural manipulations [12,36–44]. Most of this work has focused on changes in vitals signs, such as heart rate, blood pressure, and/or arterial oxygen saturation, although some groups have also used near-infrared spectroscopy to probe cerebrovascular oxygenation during postural change [42–44]. To this author’s knowledge, only one study by Anthony et al. has been done with Doppler ultrasound to monitor the effects of HOB elevation to cerebral blood flow velocity in the main arteries [12], and no experiment has examined the resulting changes in microvascular cerebral blood flow. Anthony et al. defined four classifications of peak systolic velocity response within 30 seconds of a 20° HOB elevation: (1) a sudden change within 5 seconds, (2) a sudden change within 5 seconds, followed by a corrective change, (3) no change, (4) cycling or no discernible trace. Responses 2 and 3 were the most common. Two major questions raised by this finding are: what relationships exist between these large-vessel changes and the microvascular cerebral blood flow, and what impact, if any, does postural manipulation have on this microvascular flow. Our present study aims to address both of these questions by examining the use of diffuse correlation spectroscopy on preterm neonates.

In order to monitor speckle fluctuations in time, we measure the normalized intensity autocorrelation function, g ₂(r, τ) = I(r,t)I(r, t + τ) / I(r, t)², and calculate the normalized electric field temporal autocorrelation function, g ₁(r, τ) = G ₁(r, τ)/ E ^*(r, t)E(r, t), using the Siegert relation, g ₂(r, τ) = 1 + β |g ₁(r, τ)|². Here I(r, t) is the intensity at time t and position r, E(r, t) is the electric field at time t and position r, denotes the ensemble or time average, and β is a constant that depends on the source coherence, detection optics, ambient light and other external factors. G ₁(r, τ) is the unnormalized electric field autocorrelation function, equal to E ^*(r,t)E(r, t + τ), which obeys a correlation diffusion equation [18,19]. In this study we assume a homogeneous tissue-air interface geometry in the plane z = 0. The semi-infinite solution of G ₁(r, τ) to the correlation diffusion equation for a point source of the form S(r) = S ₀δ(r) [19,29] isHere ; μ_a (cm⁻¹) and are the tissue absorption and reduced scattering coefficients, respectively; k ₀ (cm⁻¹) is the magnitude of the optical wave vector, 2πn/λ, where n is the index of refraction of tissue and λ is the wavelength of incident light; α represents the percentage of light scattering events that come from moving scatterers; is the mean squared displacement of the moving scatterers in time τ, assumed to have the form 6D_Bτ where D_B is an effective Brownian diffusion coefficient; r ₁ and r ₂ (cm) are the distances between the detector and the source/image source, respectively, i.e. and ; is the depth at which a collimated source on the tissue surface can be approximated as a point source; in the case of refraction indices of tissue and air (equal to 1.4 and 1.0, respectively). The decay rate of this autocorrelation function is dictated by blood flow. We define a blood flow index, BFI αD_B, with units of cm²/s, to quantify this decay rate. BFI reflects CBF, and changes in BFI relative to baseline measurements (rBFI) reflect analogous changes in CBF (rCBF) [30]. Although this analysis approach is empirical, numerous studies have validated it as a measure of relative blood flow, including comparisons to literature [21–23], to Doppler ultrasound in murine tumors [26], to laser Doppler flowmetry in rat brain [21–23,28], to fluorescent microspheres in piglet brains [35], and to arterial spin-labeled perfusion MRI in human brain and calf muscle [24,32].

The DCS instrument uses a long-coherence-length laser (CrystaLaser, RCL-080-785S) operating at 785 nm to deliver light to the tissue. A single mode fiber secured by a black foam pad detects light 1.5 cm from the source. The fiber is custom designed with a 90° bend on the patient end, permitting the probe to rest adjacent to the forehead. Light is detected by a fast photon counting avalanche photodiode that outputs a TTL signal for every photon received. This TTL signal is transmitted to a custom built 2-channel correlator board (FLEX03OEM2CH, correlator.com, Bridgewater, NJ) derives the intensity autocorrelation function based on the photon arrival times [45]. Figure 1 shows a sketch of the probe on the infant’s head.

Protocols for both DCS and TCD measurements are shown in Figure 2. Head of bed (HOB) angle manipulations were limited by the range of the isolette beds (Ohio Care, Ohmeda Isolette). These beds easily adjust from an angle of 0° to approximately 12° in seconds.

For DCS, data were acquired for 30 minutes: three 5 minute HOB=0° sessions, alternating with three 5 minute HOB=12° sessions. Intensity autocorrelation curves were averaged over a period of 3 seconds and were acquired every 7 seconds, resulting in around 300 BFI data points per study. These curves were then converted to electric field autocorrelation functions using the Siegert relation with a fitted value for β of approximately 0.5. Theoretically, we expect β to be approximately equal to the inverse of the number of modes allowed to pass through the detection optics. In our case of single mode detection fibers, the number of modes is two. For analysis, we solved the correlation diffusion equation analytically, assuming the sample geometry was a homogeneous, semi-infinite medium. We used the semi-infinite solution (Equation 1) to fit our data for BFI. This fit is made possible by assuming constant values for μ_a and (0.1 cm⁻¹ and 10 cm⁻¹, respectively, at 785 nm) for the whole population and throughout the study. These values were chosen from literature references [47,48].

A mean relative change in CBF was calculated after each HOB= 12° event using the preceding HOB= 0° event for a baseline, i.e. for the i^th repetition, , where indicates the mean BFI over all data points taken at the i^th HOB= θ° event. After was calculated for each HOB change (3 total per day of study), a mean relative change in cerebral blood flow, rCBF, was determined for each day. Here denotes the mean value over all measured on a given day. Additionally, to compare absolute measurements of BFI to the velocities found from TCD, a mean blood flow index, , was calculated for each day of study. For the purposes of this analysis, we used the initial HOB= 0° data to calculate , i.e. , since the baby was most peaceful during this time period.

The protocol for TCD measurements differed slightly from the DCS protocol. Only two or three measurements of peak systolic velocity (PSV), end diastolic velocity (EDV), and mean velocity (MV = (PSV − EDV)/PI, where PI is the pulsatility index measured by the ultrasound scanner), were obtained at each head of bed angle. A Philips ATL HDI 5000 ultrasound scanner (Philips Medical Systems, Bothell, WA) with a C8-5 MHz broadband curved array transducer was used for data acquisition. Typically this process was repeated twice. As in the case of the mean rCBF calculation, mean relative changes of each parameter for the i^th repetition comparing the responses at elevated HOB to lying flat were computed after each HOB change, i.e. . Daily mean changes in velocities, rPSV, rEDV, rMV, were calculated in the same manner as rCBF. For comparison to DCS, mean velocities at the initial HOB= 0° event (, , and ) were calculated for each day of study. Unfortunately TCD and DCS data could not be collected at the same time due to the size of the infant’s head. However, the data were acquired on the same day by both modalities.

While absolute values of BFI from DCS and velocities from TCD correlate significantly, correlations between relative changes in these parameters did not achieve significance. It is not entirely clear why the absolute velocity measurements correlate well with BFI, while relative changes do not correlate with rCBF. We hypothesize that there may have been insufficient statistical power given the limited sample size and relatively large physiological and measurement noise as well as inter- and intra-subject variability. Future experiments would benefit from concurrent TCD and DCS data acquisition.

Our TCD results of relative velocity changes concur with Anthony et al. [12], who showed that most infants showed little or no middle cerebral artery velocity response to postural changes. However, a direct comparison between the studies is difficult because Anthony et al. used a larger bed elevation (20°) and monitored continuously before and after the bed tilt for only 30 seconds.

For the future, DCS quantification can be improved. For example, a semi-infinite model was used to fit DCS data. This model simplifies the head geometry of the infants. In reality, the scalp, skull, cerebral spinal fluid, grey matter, and white matter possess different optical properties that can be accounted for, at least partially, in calculations [21,27,46]. Although the semi-infinite model can be improved upon, it provides a sufficient approximation for this pilot study.