4.6 Article

Expanded modeling of temperature-dependent dielectric properties for microwave thermal ablation

期刊

PHYSICS IN MEDICINE AND BIOLOGY
卷 56, 期 16, 页码 5249-5264

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IOP PUBLISHING LTD
DOI: 10.1088/0031-9155/56/16/011

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资金

  1. National Institute of Health [1 R01 142737]

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Microwaves are a promising source for thermal tumor ablation due to their ability to rapidly heat dispersive biological tissues, often to temperatures in excess of 100 degrees C. At these high temperatures, tissue dielectric properties change rapidly and, thus, so do the characteristics of energy delivery. Precise knowledge of how tissue dielectric properties change during microwave heating promises to facilitate more accurate simulation of device performance and helps optimize device geometry and energy delivery parameters. In this study, we measured the dielectric properties of liver tissue during high-temperature microwave heating. The resulting data were compiled into either a sigmoidal function of temperature or an integration of the time-temperature curve for both relative permittivity and effective conductivity. Coupled electromagnetic-thermal simulations of heating produced by a single monopole antenna using the new models were then compared to simulations with existing linear and static models, and experimental temperatures in liver tissue. The new sigmoidal temperature-dependent model more accurately predicted experimental temperatures when compared to temperature-time integrated or existing models. The mean percent differences between simulated and experimental temperatures over all times were 4.2% for sigmoidal, 10.1% for temperature-time integration, 27.0% for linear and 32.8% for static models at the antenna input power of 50W. Correcting for tissue contraction improved agreement for powers up to 75 W. The sigmoidal model also predicted substantial changes in heating pattern due to dehydration. We can conclude from these studies that a sigmoidal model of tissue dielectric properties improves prediction of experimental results. More work is needed to refine and generalize this model.

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