We present a radio-frequency-activated switching system that can automatically detune a metamaterial resonator to enhance magnetic resonance imaging (MRI) performance. Local sensitivity-enhancing metamaterials typically consist of resonant components, which means that the transmitted radio frequency field is spatially inhomogeneous. The switching system shows for the first time that a metamaterial resonator can be detuned during transmission and tuned during reception using a digital circuit. This allows a resonating system to maintain homogeneous transmit field while maintaining an increased receive sensitivity. As a result, sensitivity can be enhanced without changing the system-provided specific absorption rate (SAR) models. The developed digital circuit consists of inductors sensitive to the transmit radio-frequency pulses, along with diodes acting as switches to control the resonance frequency of the resonator. We first test the automatic resonator detuning on-the-bench, and subsequently evaluate it in a 1.5 T MRI scanner using tissue-mimicking phantoms. The scan results demonstrate that the switching mechanism automatically detunes the resonator in transmit mode, while retaining its sensitivity-enhancing properties (tuned to the Larmor frequency) in receive mode. Since it does not require any connection to the MRI console, the switching system can have broad applications and could be adapted for use with other types of MRI scanners and field-enhancing resonators.

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We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.