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Because of its ability to measure the temperature-dependent power of electromagnetic radiation emitted from tissue
down to several centimeters beneath the skin, microwave radiometry has long been of interest as a means for identifying
the internal tissue temperature anomalies that arise from abnormalities in physiological parameters such as metabolic and
blood perfusion rates. However, the inherent lack of specificity and resolution in microwave radiometer measurements
has limited the clinical usefulness of the technique. The idea underlying this work is to make use of information
(assumed to be available from some other modality) about the tissue configuration in the volume of interest to study and
improve the accuracy of anomaly detection and estimation from radiometric data. In particular, knowledge of the
specific anatomy and the properties of the overall measurement system enable determination of the signatures of
localized physiological abnormalities in the radiometry data. These signatures are used to investigate the accuracy with
which the location of an anomaly can be determined from radiometric measurements. Algorithms based on matches to
entries in a signature dictionary are developed for anomaly detection and estimation. The accuracy of anomaly
identification is improved when the coupling of power from the body to the sensor is optimized. We describe the design
of a radiometer waveguide having dielectric properties appropriate for biomedical applications.
Patrick Kelly,Tamara Sobers,Benjamin St. Peter,Paul Siqueira, andGeoffrey Capraro M.D.
"Microwave radiometric signatures of temperature anomalies in tissue", Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 831368 (3 March 2012); https://doi.org/10.1117/12.910785
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Patrick Kelly, Tamara Sobers, Benjamin St. Peter, Paul Siqueira, Geoffrey Capraro M.D., "Microwave radiometric signatures of temperature anomalies in tissue," Proc. SPIE 8313, Medical Imaging 2012: Physics of Medical Imaging, 831368 (3 March 2012); https://doi.org/10.1117/12.910785