ABSTRACT
On-body antennas for use in microwave imaging (MI) systems can direct energy around the body instead of through the body, thus degrading the overall signal-to-noise ratio (SNR) of the system. This work introduces and quantifies the usage of modern metal-backed RF absorbing foam in conjunction with on-body antennas to dampen energy flowing around the body, using both simulations and experiments. A head imaging system is demonstrated herein but the principle can be applied to any part of the body including the torso or extremities. A computational model was simulated numerically using Ansys HFSS. A physical prototype in the form of a helmet with embedded antennas was built to compare simulations with measured data. Simulations and measurements demonstrate that usage of such metal-backed RF-absorbing foams can significantly reduce around-body coupling from Transmit (Tx) and Receive (Rx) antennas by approximately 10dB. Thus, the overall SNR of the MI system can be substantially improved using this low-cost and affordable method.
ABSTRACT
An on-body antenna, comprised of two closely-spaced antiphase patch elements, for microwave imaging may provide enhanced signal penetration into the tissue. By further integrating a 180-degree on-chip power combiner with the dual antiphase patch antenna element, a low-profile miniaturized antenna, integrated into a single 18.5 mm x 10 mm x 1.6 mm circuit board assembly, is designed and evaluated both numerically and experimentally. This is the smallest on-body antenna known to the authors for the given frequency band. This linearly polarized antenna may potentially serve as a building block of a dense antenna array for prospective high-resolution microwave imaging. A 2.4 GHz band was chosen as the design target. The final antenna size was a compromise between the miniaturization, the SNR (Signal-to-Noise Ratio), and the targeted antenna bandwidth (2.3-2.5 GHz). The effect of surface waves (the secondary radiating components) was also factored in the design consideration, while maximizing the detected signals' SNR.
ABSTRACT
OBJECTIVE: There is an unmet need for quick, physically small, and cost-effective office-based techniques that can measure bone properties without the use of ionizing radiation. METHODS: The present study reports the application of a neural network classifier to the processing of previously collected data on very-low-power radiofrequency propagation through the wrist to detect osteoporotic/osteopenic conditions. Our approach categorizes the data obtained for two dichotomic groups. Group 1 included 27 osteoporotic/osteopenic subjects with low Bone Mineral Density (BMD), characterized by a Dual X-Ray Absorptiometry (DXA) T-score below - 1, measured within one year. Group 2 included 40 healthy and mostly young subjects without major clinical risk factors such as a (family) history of bone fracture. We process the complex radiofrequency spectrum from 30 kHz to 2 GHz. Instead of averaging data for both wrists, we process them independently along with the wrist circumference and then combine the results, which greatly increases the sensitivity. Measurements along with data processing require less than 1 min. RESULTS: For the two dichotomic groups identified above, the neural network classifier of the radiofrequency spectrum reports a sensitivity of 83% and a specificity of 94%. SIGNIFICANCE: These results are obtained without including any additional clinical risk factors. They justify that the radio transmission data are usable on their own as a predictor of bone density. This approach has the potential for screening patients at risk for fragility fractures in the office, given the ease of implementation, small device size, and low costs associated with both the technique and the equipment.
