Abstract
Near-infrared spectroscopy (NIRS) estimations of the adult brain baseline optical properties based on a homogeneous model of the head are known to introduce significant contamination from extracerebral layers. More complex models have been proposed and occasionally applied to in vivo data, but their performances have never been characterized on realistic head structures. Here we implement a flexible fitting routine of time-domain NIRS data using graphics processing unit based Monte Carlo simulations. We compare the results for two different geometries: a two-layer slab with variable thickness of the first layer and a template atlas head registered to the subject's head surface. We characterize the performance of the Monte Carlo approaches for fitting the optical properties from simulated time-resolved data of the adult head. We show that both geometries provide better results than the commonly used homogeneous model, and we quantify the improvement in terms of accuracy, linearity, and cross-talk from extracerebral layers.
Highlights
Realistic TD-near-infrared spectroscopy (NIRS) datasets were generated using graphical processing unit (GPU)-based Monte Carlo simulations on adult heads whose structures were obtained from magnetic resonance imaging (MRI) anatomical scans
Contrary to a previous study where we reported the performance of the frequency-domain multidistance (FDMD) approach at one specific location on the head,[28] in the present work, we show a systematic characterization of the TD-NIRS fitting methods at various locations over the whole head
We generated a library of Monte Carlo data for the slab geometry, which can be used for any subject
Summary
Various continuous-wave (CW),[1,2,3,4,5] frequency-domain (FD),[6,7,8,9,10,11,12,13,14] and time-domain (TD)[15,16,17,18,19] near-infrared spectroscopy (NIRS) approaches offer the ability to determine the absolute absorption and scattering coefficients of biological tissue. The frequency-domain multidistance (FDMD) approach based on a homogeneous model[6] has been extensively validated with Monte Carlo simulations,[14] phantoms,[6,12] and animal models.[11,13] It has been successfully applied to the monitoring of brain oxygenation and metabolism in healthy and brain-injured infants.[8,9,20] Time-resolved approaches are generally based on the nonlinear fit of temporal point spread functions (TPSFs) They have been validated with Monte Carlo simulations and phantoms,[15,23,24] and have been applied to monitor developmental cerebral changes in infants.[22] In adults, the TD-NIRS technology has been applied to monitor variations in brain oxygenation and blood volume during cardiopulmonary
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