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. The spatial sensitivity profiles of photons in the head, the so-called banana shape, and the partial mean optical path lengths in the skin-scalp and brain tissues were calculated, so that the contribution of different parts of the head to near-infrared spectroscopy signals could be examined. It was shown that the signal detected in brain function measurements was greatly affected by the heterogeneity of the head tissue and its scattering properties, particularly for the shorter interfibre distances.

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. The spatial sensitivity of near-infrared spectroscopy measurement to regions in the brain, as well as the effect of optical fiber arrangement on the regions of measurement are investigated. It is shown that the detected signal in brain imaging measurements is greatly affected by the heterogeneity of the head tissue and its scattering properties.

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. For this purpose, the principal components analysis and the Karhunen-Loève transform were applied to signals acquired on PAOD and healthy subjects. Our main purpose was to have the two methods undergo a comparison that reveals the capacity of each method to interpret the characteristics of the signals used to make diagnosis. The results show that the use of the Karhunen-Loève transform method is more justified than the principal components analysis whenever we want to reduce the dimensional space of the set of initial data and still preserve the quantitative and relative proportions of the original variances associated to those data representing the laser Doppler flowmetry signal before and after its reconstruction. However, the principal components analysis method is more justified when one or several of the initial data present variances either too insignificant or too important in comparison with the other data.