Complete head cerebral sensitivity mapping for diffuse correlation spectroscopy using subject-specific magnetic resonance imaging models

Abstract

We characterize cerebral sensitivity across the entire adult human head for diffuse correlation spectroscopy, an optical technique increasingly used for bedside cerebral perfusion monitoring. Sixteen subject-specific magnetic resonance imaging-derived head models were used to identify high sensitivity regions by running Monte Carlo light propagation simulations at over eight hundred uniformly distributed locations on the head. Significant spatial variations in cerebral sensitivity, consistent across subjects, were found. We also identified correlates of such differences suitable for real-time assessment. These variations can be largely attributed to changes in extracerebral thickness and should be taken into account to optimize probe placement in experimental settings.

Functional Time Domain Diffuse Correlation Spectroscopy

“…During pressure modulation, the reduction was only 23 ± 4% at the 1.5 cm separation late gate compared to 57 ± 6% at the short (0.5 cm) separation, indicating a substantial degree of CBF sensitivity, thus giving confidence that the signals measured at late gates are derived from cerebral signals. We also note that our results matched the recent simulation study ( Wu et al, 2022 ) that found lower extracerebral thickness at the lateral side of the forehead results in higher sensitivity with respect to more medial locations. This is in good agreement with our pressure modulation task results where we found a 6.7 ± 2.8% greater reduction with pressure modulation in the medial locations compared to the lateral locations indicating somewhat higher extracerebral contribution.…”

Diffuse correlation spectroscopy: current status and future outlook

“…To illustrate, Fig. 2(a) shows the distribution of the straight line distance between the scalp surface and gray matter in different areas of the head, derived from a set of segmented MRI scans collected as part of a previous study (16 subjects, average age 29, range 25 to 41), 35 and subplots shown in Figs. 2(b) and 2(c) show the fractional recovery of a true blood flow change in the scalp and brain tissue, respectively, using simulation data from the same study 35 for the 25-mm source-detector separation used in the majority of published DCS investigations.…”

“…2(a) shows the distribution of the straight line distance between the scalp surface and gray matter in different areas of the head, derived from a set of segmented MRI scans collected as part of a previous study (16 subjects, average age 29, range 25 to 41), 35 and subplots shown in Figs. 2(b) and 2(c) show the fractional recovery of a true blood flow change in the scalp and brain tissue, respectively, using simulation data from the same study 35 for the 25-mm source-detector separation used in the majority of published DCS investigations. As can be seen in these graphs, not only is the average brain sensitivity fairly low (on the order of 20% for typical scalp to brain distances in the frontal region for example), but the measurement has higher sensitivity to scalp than to brain blood flow.…”

“…2 ), assuming the same optical properties as Ref. 35 and photon count rates typical of our experimental data (11 kcps at 25 mm, scaled across other distances based on light fluence estimations). A step change in CBF was simulated versus baseline conditions, and CNR was defined as the fraction of the true cerebral perfusion change recovered using DCS measurements (under a homogeneous medium assumption) divided by the standard deviation of the estimate.…”

“…We thank Melissa Wu for useful discussions and for facilitating further analysis of the segmented MRI data from Ref. 35 .…”