Monte Carlo based modeling of indocyanine green bolus tracking in the adult human head

摘要

The use of near-infrared spectroscopy (NIRS) is increasingly being investigated in critical care settings to assess cerebral hemodynamics, because of its potential for guiding therapy during the recovery period following brain injury. Cerebral blood flow (CBF) can be quantified by NIRS using indocyanine green (ICG) as an intravascular tracer. However, extracting accurate measurements from complex tissue geometries, such as the human head, is challenging and has hindered the clinical applications. With the development of fast Monte Carlo simulations that can take into account a priori anatomical information (e.g. near-infrared light propagation in tissue from MRI or CT imaging data), it is now possible to investigate signal contamination arising from the extracerebral layers, which can confound NIRS-CBF measurements. Here, we present a theoretical model that combines Monte Carlo simulations of broadband time-resolved near-infrared measurements with indicator-dilution theory to model time-dependent changes in light propagation following ICG bolus injection. Broadband, time-resolved near-infrared spectroscopy measurements were simulated for three source-detector positions. Individual simulations required 56 seconds for 5x108 photons, and a set of simulations consisting of baseline measurements at 40 wavelengths, and single-wavelength measurements at 160 time-points required on average 3.4 hours. To demonstrate the usefulness of our model, the propagation of errors associated with varying both the scalp blood flow and the scalp thickness was investigated. For each simulation the data were analyzed using four independent approaches—simple-subtraction blood flow index (ABFIss), time-resolved variance time-to-peak (ATTPTR), and absolute and relative CBF with depth-resolved NIRS (CBFDR and ACBFDR)—to assess cerebral hemodynamics.