Depth sensitivity analysis of functional near-infrared spectroscopy measurement using three-dimensional Monte Carlo modelling-based magnetic resonance imaging.

Theoretical analysis of spatial distribution of near-infrared light propagation in head tissues is very important in brain function measurement, since it is impossible to measure the effective optical path length of the detected signal or the effect of optical fibre arrangement on the regions of measurement or its sensitivity. In this study a realistic head model generated from structure data from magnetic resonance imaging (MRI) was introduced into a three-dimensional Monte Carlo code and the sensitivity of functional near-infrared measurement was analysed. The effects of the distance between source and detector, and of the optical properties of the probed tissues, on the sensitivity of the optical measurement to deep layers of the adult head were investigated.

Spatial sensitivity of near-infrared spectroscopic brain imaging based on three-dimensional Monte Carlo modeling.

Accurate estimation of the radiation distribution in the adult human head requires realistic head models generated from magnetic resonance imaging (MRI) scans with true optical properties of each layer of the head. In this study, a complex three-dimensional structural data obtained by MRI are introduced in a three-dimensional Monte Carlo code, with varying optical properties and arbitrary boundary condition, to calculate the spatial sensitivity profile of photon in head, so-called banana-shaped. It is therefore a better model to incorporate the contribution of cerebrospinal fluid (CSF) when modeling the head.

In vivo comparison between the principal components analysis and the Karhunen-Loève transform as methods used for the de-noising of laser Doppler reactive hyperemia signals.

This contribution shows the comparison of two methods, the principal components analysis and the Karhunen-Loève transform. Indeed, reactive hyperemia signals obtained with laser Doppler flowmetry are currently used to diagnose peripheral arterial occlusive diseases (PAOD), but they are not noise-free. De-noising of such signals could lead to an improved diagnosis.