Abstract
.Significance: Contamination of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow (CBF) due to systemic physiology remains a significant challenge in the clinical translation of DCS for neuromonitoring. Tunable, multi-layer Monte Carlo-based (MC) light transport models have the potential to remove extracerebral flow cross-talk in cerebral blood flow index () estimates.Aim: We explore the effectiveness of MC DCS models in recovering accurate changes in the presence of strong systemic physiology variations during a hypercapnia maneuver.Approach: Multi-layer slab and head-like realistic (curved) geometries were used to run MC simulations of photon propagation through the head. The simulation data were post-processed into models with variable extracerebral thicknesses and used to fit DCS multi-distance intensity autocorrelation measurements to estimate timecourses. The results of the MC values from a set of human subject hypercapnia sessions were compared with values estimated using a semi-infinite analytical model, as commonly used in the field.Results: Group averages indicate a gradual systemic increase in blood flow following a different temporal profile versus the expected rapid CBF response. Optimized MC models, guided by several intrinsic criteria and a pressure modulation maneuver, were able to more effectively separate changes from scalp blood flow influence than the analytical fitting, which assumed a homogeneous medium. Three-layer models performed better than two-layer ones; slab and curved models achieved largely similar results, though curved geometries were closer to physiological layer thicknesses.Conclusion: Three-layer, adjustable MC models can be useful in separating distinct changes in scalp and brain blood flow. Pressure modulation, along with reasonable estimates of physiological parameters, can help direct the choice of appropriate layer thicknesses in MC models.
Highlights
The brain receives 12% to 15% of cardiac output even though it weighs only 2% of the body weight.[1,2] Cerebral blood flow (CBF) is responsible for brain oxygen delivery, and accurate, continuous CBF quantification can provide crucial information for monitoring brain health and function.[3,4] This is important under conditions when cerebral autoregulation may be impaired, potentially leading to insufficient blood flow to the brain.[4,5] CBF can be a useful biomarker for both diagnosing and managing patients suffering from stroke, traumatic brain injury, or other neurological impairments.[6]
This paper aims to evaluate the effectiveness of Monte Carlo-based (MC) fitting methods in removing extracerebral contaminants from Diffuse correlation spectroscopy (DCS) cerebral blood flow index (CBFi) estimates
From there we looked at measurements with two distinct features in the observed responses to the hypercapnia administration: first, a significant (>20%) and persistent increase in superficial blood flow index (BFi), lasting well beyond the end of CO2 administration; and second, a long separation response showing a different temporal profile to what was observed in the 5-mm detector, such as an earlier/higher peak, or a decrease during recovery not observed in the short separation detector
Summary
The brain receives 12% to 15% of cardiac output even though it weighs only 2% of the body weight.[1,2] Cerebral blood flow (CBF) is responsible for brain oxygen delivery, and accurate, continuous CBF quantification can provide crucial information for monitoring brain health and function.[3,4] This is important under conditions when cerebral autoregulation may be impaired, potentially leading to insufficient blood flow to the brain.[4,5] CBF can be a useful biomarker for both diagnosing and managing patients suffering from stroke, traumatic brain injury, or other neurological impairments.[6]. Diffuse correlation spectroscopy (DCS) is becoming increasingly widespread as a noninvasive optical technology to measure tissue perfusion, in the brain.[3,7] A long coherence-length laser emitting light in the near-infrared range is used to illuminate the probed tissue region, and photon counting detectors are used to detect speckle fluctuations in the light; the temporal autocorrelation of these fluctuations in the reflected light can be used to characterize the motion of light scatterers in the medium, in this case red blood cells.[5,8] DCS takes advantage of this physical phenomenon to monitor time-varying blood flow non-invasively and is currently being used in various research applications.[5,8,9,10,11,12,13,14,15] A successful application area is the CBF monitoring of neonates, for whom the relatively thin skull results in high brain sensitivity.[16,17]
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