Revisiting equivalent optical properties for cerebrospinal fluid to improve diffusion-based modeling accuracy in the brain.

Abstract

Significance: The diffusion approximation (DA) is used in functional near-infrared spectroscopy (fNIRS) studies despite its known limitations due to the presence of cerebrospinal fluid (CSF). Many of these studies rely on a set of empirical CSF optical properties, recommended by a previous simulation study, that were not selected for the purpose of minimizing DA modeling errors.

Aim: We aim to directly quantify the accuracy of DA solutions in brain models by comparing those with the gold-standard solutions produced by the mesh-based Monte Carlo (MMC), based on which we derive updated recommendations.

Approach: For both a five-layer head and Colin27 atlas models, we obtain DA solutions by independently sweeping the CSF absorption ( ${\mu }_a$ ) and reduced scattering ( ${\mu }_{s^{\text{'}}}$ ) coefficients. Using an MMC solution with literature CSF optical properties as a reference, we compute the errors for surface fluence, total brain sensitivity, and brain energy deposition, and identify the optimized settings where such error is minimized.

Results: Our results suggest that previously recommended CSF properties can cause significant errors (8.7% to 52%) in multiple tested metrics. By simultaneously sweeping ${\mu }_a$ and ${\mu }_s^{\text{'}}$ , we can identify infinite numbers of solutions that can exactly match DA with MMC solutions for any single tested metric. Furthermore, it is also possible to simultaneously minimize multiple metrics at multiple source/detector separations, leading to our updated recommendation of setting ${\mu }_s^{\text{'}}=0.15{\mathrm{mm}}^{-1}$ while maintaining physiological ${\mu }_a$ for CSF in DA simulations.

Conclusions: Our updated recommendation of CSF equivalent optical properties can greatly reduce the model mismatches between DA and MMC solutions at multiple metrics without sacrificing computational speed. We also show that it is possible to eliminate such a mismatch for a single or a pair of metrics of interest.