Temperature dependence of thermal properties of ex vivo liver tissue up to ablative temperatures

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Lopresto V., Argentieri A., Pinto R., Cavagnaro M.
ISSN: 0031-9155

Thermal properties of ex vivo bovine liver were measured as a function of temperature, by heating tissue samples in a temperature-controlled oil bath over a temperature range from about 21 °C to about 113 °C. Results evidenced temperature-dependent non-linear changes of the thermal properties, with the temperature of 100 °C representing a break point: the thermal properties increased with temperature up to 99 °C and then decreased above 100 °C. The rate of increase appeared dramatic between 90 °C and 99 °C, owing to the onset of vaporisation of water contained in the tissue. In particular, at 99 °C, the thermal conductivity reported an increase of about four times with respect to the value measured at 90 °C, whilst about a two-fold increase was reported for both the volumetric heat capacity and the thermal diffusivity. Temperatures higher than 100 °C were reached only after complete vaporisation of water contained in the tissue, resulting in about 70% loss of weight from the tissue. An overall decrease of about 71% and 63% was reported for the thermal conductivity and volumetric heat capacity, respectively, in the temperature range 101 °C-113 °C. A decrease of about 25% was reported in the measured values of the thermal diffusivity in the temperature range 101 °C-108 °C, whilst a slight increase of measured values, not statistically significant, was observed in the temperature range 108 °C-113 °C. The temperature dependent changes of the thermal parameters were modelled with non-linear regression analysis to calculate the best-fit curves interpolating measured data. The proposed regression models could be used to numerically assess the changes in the thermal properties of biological tissues at supra-physiological temperatures relevant in thermal ablation procedures, as well as their effect on the prediction of the ablation zone dimensions in computational models for treatment planning.

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