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.
Highlights
Patients requiring intensive care due to life-threatening neurological emergencies, such as ischemic stroke, traumatic brain injury and subarachnoid hemorrhage, are at high risk of secondary brain injury [1,2,3]
The current study investigates the application of a multi-layered (ML) Diffuse correlation spectroscopy (DCS) model to data acquired at multiple source-detector distances with the aim of separating brain and scalp blood flow (SBF)
4.1 Phantom experiments Figure 4 illustrates the relative change in the estimated diffusion coefficient from the homogeneous model (A) and the ML DCS model (B) as the viscosity in the bottom layer of the phantom was increased by adding cellulose
Summary
Patients requiring intensive care due to life-threatening neurological emergencies, such as ischemic stroke, traumatic brain injury and subarachnoid hemorrhage, are at high risk of secondary brain injury [1,2,3]. Multiple factors contribute to poor outcome, a major focus is preventing delayed cerebral ischemia. A major focus of neurointensive care is maintaining adequate cerebral blood flow (CBF) through treatments such as administering nimodipine, inducing hypertension, and intervening with surgical or pharmacological angioplasty [5]. A key component of patient management is the use of monitoring techniques to detect signs of impaired CBF, such as elevated flow velocities in cerebral arteries as measured by transcranial Doppler. This is not a direct measure of CBF and cerebral ischemia can occur without evidence of arterial narrowing [6]. There remains no established bedside technique capable of monitoring CBF
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