Peer-Reviewed Journal Details
Mandatory Fields
Robert Balduino, Barry McDermott, Emily Porter, Muhammad Adnan Elahi, Atif Shahzad, Martin O'Halloran, Marta Cavagnaro
Ieee Transactions On Dielectrics And Electrical Insulation
Feasibility of Water Content-Based Dielectric Characterisation of Biological Tissues using Mixture Models
Optional Fields
biodielectrics, dielectric properties, mixture models
This study quantitatively examines the validity of mixture formulae as models to describe the microwave-range dielectric properties of biological tissue of varying water content. Mixture formulae, specifically the Maxwell Garnett and Bruggeman models, are used to predict the dielectric properties of ex-vivo bovine muscle and liver tissue samples varying in water content. The tissues are modelled as comprising of cell and macromolecule inclusions in a water matrix. The model predictions are compared to dielectric measurements performed with a network analyser and dielectric probe in the 0.5 Ė 8.5 GHz band. There was a poor match between the properties predicted by the models and the measured results at most frequency points, for both tissue types. However, the overall predicted and measured trends over the measured band correlated well. The Maxwell Garnett and Bruggeman models may prove a valuable tool aiding in the characterisation of the dielectric properties of materials with different water contents, however, currently, direct application of the model with the assumption of solid inclusions in a water matrix is not feasible without substantial improvement to the models. The dielectric properties of biological tissue are of fundamental importance for many medical technologies ranging from diagnostic to therapeutic. There is a need for continuous improvements to be made to the techniques used to measure and characterise the dielectric properties of tissues. Mixture models are investigated in this study as potentially a valuable candidate modelling technique for the dielectric profiling of tissues based on water content.
Grant Details
The research leading to these results has received funding from the European Research Council under the European Unionís Horizon 2020 Programme/ ERC Grant Agreement BioElecPro n.637780, Science Foundation Ireland (SFI) grant number 15/ERCS/3276, the Hardiman Research Scholarship from NUIG, the charity RESPECT and the People Programme (Marie Curie Actions) of the European Unionís Seventh Framework Programme (FP7/2007-2013) under REA Grant Agreement no. PCOFUND-GA-2013-608728.
Publication Themes
Biomedical Science and Engineering