Assessment of a multi-layered diffuse correlation spectroscopy method for monitoring cerebral blood flow in adults

Abstract

Diffuse correlation spectroscopy (DCS) is a promising technique for brain monitoring as it can provide a continuous signal that is directly related to cerebral blood flow (CBF); however, signal contamination from extracerebral tissue can cause flow underestimations. The goal of this study was to investigate whether a multi-layered (ML) model that accounts for light propagation through the different tissue layers could successfully separate scalp and brain flow when applied to DCS data acquired at multiple source-detector distances. The method was first validated with phantom experiments. Next, experiments were conducted in a pig model of the adult head with a mean extracerebral tissue thickness of 9.8 ± 0.4 mm. Reductions in CBF were measured by ML DCS and computed tomography perfusion for validation; excellent agreement was observed by a mean difference of 1.2 ± 4.6% (CI95%: -31.1 and 28.6) between the two modalities, which was not significantly different.

Keywords: (170.1470) Blood or tissue constituent monitoring; (170.3660) Light propagation in tissues; (170.3880) Medical and biological imaging; (170.6935) Tissue characterization.

Figures

Fig. 1

A wire diagram of the constructed two-layered phantom. With the box tipped on its side, cellulose could be added to either layer layer via the open side. The mobile Mylar membrane could be removed by the open side or inserted into slots at 5 or 10 mm relative to the surface that included the optical fibers.

Fig. 2

A diagram of the time course for a typical experiment. Blood gas analysis (BGA) was performed to confirm capnia level, where normo and hypo represent normocapnia and hypocapnia, respectively. Data acquisitions (DA) are listed in sequential order; TR NIRS (not shown in the diagram) is always acquired between CTP and DCS.

Fig. 3

Coronal CT image of a pig’s head (A) and the corresponding blood flow map (B). The scalp (1), skull (2) and brain (3) ROIs are shown in white. Bar codes are given to illustrate relative x-ray attenuation (A) and blood flow in mL/min/100g (B).

Fig. 4

Relative change in the measured diffusion coefficient as the viscosity of the tissue-mimicking phantom was increased. The label ‘expected’ refers to the homogeneous case (blue bars), and the labels ‘5 mm’ and ‘10 mm’ refer to thickness of the top layer for the two-layered case (red and green bars, respectively). For the two-layered experiments, cellulose was only added to the bottom layer. (A) Diffusion coefficient determined by analyzing the two-layered data with the homogeneous model (SDD = 30 mm). Diffusion coefficients for the bottom (B) and top (C) layers of a two-layered model applied to the same data used in (A). This analysis used data acquired at SDD of 20 and 30 mm.

Fig. 5

Normalized intensity autocorrelation functions acquired during normocapnia (red curve) and hypocapnia (blue curve) at SDD of 20 mm (A) and 27 mm (B) with count rates of ~465 and ~55 kHz, respectively. The fit of the ML DCS model is illustrated by the black curve.

Fig. 6

Scalp blood flow (SBF) and cerebral blood flow (CBF) measured by CT, and the corresponding blood flow indices measured by DCS during normocapnia (red bars) and hypocapnia (blue bars). All values were averaged over their pre- and post- scalp incision measurements. FS and FB were obtained from the ML model analysis of DCS data acquired at SDDs of 20 and 27 mm. FHM was obtained by analyzing data from each SDD separately with the HM model (FHM,1 refers to 20 mm and FHM,2 refers to 27 mm). Significant differences observed between capnic conditions are represented by *.

Fig. 7

(A) Box plot of relative flow change caused by reducing paCO2 from normocapnia to hypocapnia. Flow values of CBF, FB, FHM,1 and FHM,2 measured by CTP (N = 14), ML DCS (N = 11), DCSHM,1 (N = 14) and DCSHM,2 (N = 14), respectively. The center line, box edges, error bars, and the cross represent the median, 1st and 3rd quartiles, CI95%, and outliers, respectively. Significant changes compared to CBF are represented by *. (B) Bland-Altman plot comparing reductions in CBF and FB measured by CTP and ML DCS (N = 11). The mean difference between the two modalities, the standard error of the mean, and the CI95% are indicated by the solid line, the dotted line and the dashed line, respectively.

Fig. 8

Blood flow dynamics during the transition from normocapnia to hypocapnia, which is illustrated by the solid black vertical line, from one experiment. Each g2(ρ,τ) curve was acquired for two seconds and analyzed separately by the ML DCS model to obtain time series of FB and FS. Data were acquired at a count rate of 554.3 ± 0.4 kHz and 101.5 ± 0.2 kHz at SDDs of 20 mm and 27 mm, respectively. The thickness of the scalp and the skull were 3.7 ± 0.4 mm and 6.0 ± 0.4 mm, respectively; Fskull = 0.