Non-invasive monitoring of brain tissue temperature by near-infrared spectroscopy

Abstract

Near-infrared spectroscopy (NIRS) is a non-invasive technique that is established as a research tool with which to study tissue oxygenation and haemodynamics, particularly in infant brains. In studies of oxygenation, NIRS relies on the oxygen-dependence of the haemoglobin spectrum to determine blood oxygen saturation. Similarly, the temperature- dependence of the tissue water absorption spectrum, due to changes in intermolecular hydrogen bonding, could act as an endogenous indicator of tissue temperature. This thesis describes the development of a methodology to determine in vivo tissue temperature using NIRS, based on the temperature response of the NIR water spectrum. One particular application of this technique is monitoring cerebral temperature in infants suffering from birth asphyxia, a condition in which the brain is deprived of oxygen. It has been shown that cooling the brain by a few degrees has a neuroprotective effect and the potential to prevent long-term damage. In order to assess the efficacy of mild hypothermia, brain temperature must be monitored continuously and non-invasively during the treatment, which is in principle possible with NIRS. Experiments have been performed to accurately characterise the temperature-dependence (in the clinical range) of the NIR water absorption spectrum between 650 and 1050 nm. Measurements of absorption are calibrated against temperature using a range of multivariate fitting techniques. In order to determine tissue temperature from the calibration of the water spectrum, the contribution of absorption to in vivo NIRS measurements must be separated from that of light scattering. Furthermore, absorption due to water must be extracted from the total absorption, which contains contributions from oxy- and deoxyhaemoglobin. A number of different approaches have been explored, including second derivative spectroscopy, non-linear diffusion theory modelling and spatially-resolved techniques. The performance of the tissue prediction algorithms are investigated using temperature-resolved measurements of a tissue phantom and ex vivo and in vivo tissues

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