Sensitivity of near-infrared spectroscopy and diffuse correlation spectroscopy to brain hemodynamics: simulations and experimental findings during hypercapnia

Abstract

Near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS) are two diffuse optical technologies for brain imaging that are sensitive to changes in hemoglobin concentrations and blood flow, respectively. Measurements for both modalities are acquired on the scalp, and therefore hemodynamic processes in the extracerebral vasculature confound the interpretation of cortical hemodynamic signals. The sensitivity of NIRS to the brain versus the extracerebral tissue and the contrast-to-noise ratio (CNR) of NIRS to cerebral hemodynamic responses have been well characterized, but the same has not been evaluated for DCS. This is important to assess in order to understand their relative capabilities in measuring cerebral physiological changes. We present Monte Carlo simulations on a head model that demonstrate that the relative brain-to-scalp sensitivity is about three times higher for DCS (0.3 at 3 cm) than for NIRS (0.1 at 3 cm). However, because DCS has higher levels of noise due to photon-counting detection, the CNR is similar for both modalities in response to a physiologically realistic simulation of brain activation. Even so, we also observed higher CNR of the hemodynamic response during graded hypercapnia in adult subjects with DCS than with NIRS.

Scanning, non-contact, hybrid broadband diffuse optical spectroscopy and diffuse correlation spectroscopy system

“…On the other hand, a straightforward photon diffusion model (P 1 ) for DCS in the semi-infinite geometry was used. We did not implement a P 3 model for DCS which would have, presumably, provided a better model for short source-detector separations and higher absorption since due to the nature of DCS physics we are able to focus on higher scattering events by looking at early delay times [69]. This could be included and tested in the future.…”

Mapping breast cancer blood flow index, composition, and metabolism in a human subject using combined diffuse optical spectroscopic imaging and diffuse correlation spectroscopy

“…26 Although our absorption and scattering values were chosen to simulate breast tissue, they were varied over a large enough range to provide insight on DCS performance in other tissue types such as muscle and brain. Several phantom studies have investigated BFI error in cerebral blood flow measurements, 38,39 and they have improved BFI accuracy through more complex modeling that accounts for irregular tissue geometries. 40,41 It is possible that BFI accuracy in breast measurements can be further improved using models that account for arbitrary geometries 42 and tissue heterogeneity.…”

Analytical models for time-domain diffuse correlation spectroscopy for multi-layer and heterogeneous turbid media

“…However, if other dynamics model is adopted such as the random flow (with V 2 as the mean squared velocity of the scatterers), the present theory still holds by using (1/6)V 2 τ 2 to replace D B τ in the equations, e.g., Eqs. (1), (11) and (12). It should be noted that in recent years, more accurate models for describing the dynamics in tissue has been investigated [10, 11,23].…”

“…The measured temporal autocorrelation function of the detected light intensity is a decay curve with respect to the lag time. The decay curve contains information about the motion of the scatterers; faster decay indicates faster dynamics of the scatterers [4][5][6][7][8][9][10][11][12]. Since the light detected consists of many photons experiencing various scattering events and traveling along different paths, it is not possible for CW-DCS to differentiate photons with different pathlengths.…”