A Core Body Temperature Retrieval Method for Microwave Radiometry when Tissue Permittivity is Unknown.

This paper presents a novel method for core temperature retrieval using microwave radiometry when complex permittivity and heat transfer parameters of the tissue layers of the human subject are unknown. Previous works present methods for core temperature retrieval, but these methods do not account for population variation in the relevant electromagnetic and thermal parameters, which can increase measurement error beyond the clinically acceptable limit of 0.5°C. Pennes' bioheat model of a six-tissue-layer human head model combined with a coherent electromagnetic model simulate experimental data. To retrieve core temperature, nonlinear least squares optimization is then used to minimize the difference between the simulated experimental data and an exponential model for physical temperature and the coherent electromagnetic model. By using 20 frequencies spanning from 1-5 GHz, core temperature is retrieved while accounting for population variation in the permittivity and thermal parameters. A Monte Carlo simulation in which the thermal parameters and permittivity vary according to literature-derived, population-representative distributions and the core body temperature varies from 18-46°C is used to assess the utility of the retrieval method. Different antenna patterns are tested to explore the effect on retrieval accuracy. The retrieval method has a retrieval error of <0.1°C when only the thermal parameters are unknown and a retrieval error of <0.5°C when the thermal parameters and permittivity are unknown, which is within the clinically acceptable error range of 0.5°C. These results help progress the field of medical microwave radiometry toward being a clinically viable noninvasive measurement that is accurate when measuring all patients.

Development of a Coherent Model for Radiometric Core Body Temperature Sensing.

This paper examines the utility of a wideband, physics-based model to determine human core body or brain temperature via microwave radiometry. Pennes's bioheat equation is applied to a six-layer human head model to generate the expected layered temperature profile during the development of a fever. The resulting temperature profile is fed into the forward electromagnetic (EM) model to determine the emitted brightness temperature at various points in time. To accurately retrieve physical temperature via radiometry, the utilized model must incorporate population variation statistics and cover a wide frequency band. The effect of human population variation on emitted brightness temperature is studied by varying the relevant thermal and EM parameters, and brightness temperature emissions are simulated from 0.1 MHz to 10 GHz. A Monte Carlo simulation combined with literature-derived statistical distributions for the thermal and EM parameters is performed to analyze population-level variation in resulting brightness temperature. Variation in thermal parameters affects the offset of the resulting brightness temperature signature, while EM parameter variation shifts the key maxima and minima of the signature. The layering of high and low permittivity layers creates these key maxima and minima via wave interference. This study is one of the first to apply a coherent model to and the first to examine the effect of population-representative variable distributions on radiometry for core temperature measurement. These results better inform the development of an on-body radiometer useful for core body temperature measurement across the human population.