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Physics/Engineering

Experimental characterisation of the thermal lesion induced by microwave ablation

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Pages 110-118 | Received 08 Aug 2013, Accepted 28 Dec 2013, Published online: 26 Feb 2014
 

Abstract

Purpose: This work focuses on the characterisation of the ablated area induced by a microwave thermal ablation (MTA) procedure. An experimental methodology for establishing a straightforward correlation between the temperature gradient and the changes in the dielectric properties of the tissue is presented and discussed. Materials and methods: Temperature measurements were performed during an ablation procedure in ex vivo bovine liver, at different distances from the antenna, whereas measurements of complex permittivity were conducted in sagittal sections of the ablated samples. The measured temperatures and dielectric properties were then correlated to obtain the dependence of the dielectric properties’ spatial variation on the temperature gradient. The obtained correlation has been validated through comparison with previously obtained experimental data. A weighted cubic polynomial function and a weighted sigmoid function have been tested for best-fit interpolation of the measured data. Results: Temperatures in the range 23–105 °C were measured during the MTA procedure, while, after the end of the MTA trials, relative permittivities in the range 7–43 and electric conductivities in the range 0.3–1.8 S/m were measured according to the distance from the antenna’s axis. The polynomial function showed better regression coefficients than the sigmoid one for both the relative permittivity (R2 = 0.9947 versus R2 = 0.9912, respectively) and the conductivity (R2 = 0.9919 versus R2 = 0.9866, respectively). However, the weighted cubic function showed an unrealistic behaviour for the relative permittivity at temperatures lower than 40 °C. Conclusions: According to the results obtained, information on the changes in the dielectric properties of the tissue under MTA treatment could be inferred from measured temperature data. Once validated by in vivo studies, the proposed methodology could be exploited to develop predictive tools for treatment planning.

Acknowledgements

The authors acknowledge Sergio Mancini (ENEA, Rome) for his technical support essential to accomplish the experimental work, and Caterina Merla (ENEA) for her support in the best-fit analysis.

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