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.

Toward Non-Invasive Core Body Temperature Sensing.

This paper aims to explore the potential of a novel radiometry technique that leverages bio-matched antennas (BMAs), broadband measurements, and forward modeling of layered tissues for non-invasive and accurate core temperature monitoring. Our approach relies on the observation that electromagnetic waves penetrate to different depths depending on their frequency and dielectric properties of the medium and adapts radiative transfer models that have been successfully implemented in the past for layered geophysical media. Preliminary modeling and experimental results confirm feasibility.

Microwave Radiometry at Frequencies From 500 to 1400 MHz: An Emerging Technology for Earth Observations.

Microwave radiometry has provided valuable spaceborne observations of Earth's geophysical properties for decades. The recent SMOS, Aquarius, and SMAP satellites have demonstrated the value of measurements at 1400 MHz for observing surface soil moisture, sea surface salinity, sea ice thickness, soil freeze/thaw state, and other geophysical variables. However, the information obtained is limited by penetration through the subsurface at 1400 MHz and by a reduced sensitivity to surface salinity in cold or wind-roughened waters. Recent airborne experiments have shown the potential of brightness temperature measurements from 500-1400 MHz to address these limitations by enabling sensing of soil moisture and sea ice thickness to greater depths, sensing of temperature deep within ice sheets, improved sensing of sea salinity in cold waters, and enhanced sensitivity to soil moisture under vegetation canopies. However, the absence of significant spectrum reserved for passive microwave measurements in the 500-1400 MHz band requires both an opportunistic sensing strategy and systems for reducing the impact of radio-frequency interference. Here, we summarize the potential advantages and applications of 500-1400 MHz microwave radiometry for Earth observation and review recent experiments and demonstrations of these concepts. We also describe the remaining questions and challenges to be addressed in advancing to future spaceborne operation of this technology along with recommendations for future research activities.