Analytical sensitivity factors from distributions of time of flight of photons for near-infrared spectroscopy studies in multilayered turbid media.

Significance: In the last years, time-resolved near-infrared spectroscopy (TD-NIRS) has gained increasing interest as a tool for studying tissue spectroscopy with commercial devices. Although it provides much more information than its continuous wave counterpart, accurate models interpreting the measured raw data in real time are still lacking.

Aim: We introduce an analytical model that can be integrated and used in TD-NIRS data processing software and toolkits in real time.

Retrieval of chromophore concentration changes in a digital human head model using analytical mean partial pathlengths of photons.

Significance: Continuous-wave functional near-infrared spectroscopy has proved to be a valuable tool for assessing hemodynamic activity in the human brain in a non-invasively and inexpensive way. However, most of the current processing/analysis methods assume the head is a homogeneous medium, and hence do not appropriately correct for the signal coming from the scalp. This effect can be reduced by considering light propagation in a layered model of the human head, being the Monte Carlo (MC) simulations the gold standard to this end.

Theoretical investigation of photon partial pathlengths in multilayered turbid media.

Functional near infrared spectroscopy (fNIRS) is a valuable tool for assessing oxy- and deoxyhemoglobin concentration changes (Δ[HbO] and Δ[HbR], respectively) in the human brain. To this end, photon pathlengths in tissue are needed to convert from light attenuation to Δ[HbO] and Δ[HbR]. Current techniques describe the human head as a homogeneous medium, in which case these pathlengths are easily computed.