Microwave-based sensing for tissue analysis is recently gaining interest due to advantages such as non-ionizing radiation and non-invasiveness. We have developed a set of transmission sensors for microwave-based real-time sensing to quantify muscle mass and quality. In connection, we verified the sensors by 3D simulations, tested them in a laboratory on a homogeneous three-layer tissue model, and collected pilot clinical data in 20 patients and 25 healthy volunteers. This report focuses on initial sensor designs for the Muscle Analyzer System (MAS), their simulation, laboratory trials and clinical trials followed by developing three new sensors and their performance comparison. In the clinical studies, correlation studies were done to compare MAS performance with other clinical standards, specifically the skeletal muscle index, for muscle mass quantification. The results showed limited signal penetration depth for the Split Ring Resonator (SRR) sensor. New sensors were designed incorporating Substrate Integrated Waveguides (SIW) and a bandstop filter to overcome this problem. The sensors were validated through 3D simulations in which they showed increased penetration depth through tissue when compared to the SRR. The second-generation sensors offer higher penetration depth which will improve clinical data collection and validation. The bandstop filter is fabricated and studied in a group of volunteers, showing more reliable data that warrants further continuation of this development.

The Muscle Analyzer System (MAS) project wants to create a standalone microwave device that can assess the muscle quality, called the MAS device. To achieve that an algorithm that can derive the properties of skin, fat and muscle from the measurements is needed. This paper presents a machine learning algorithm that aims to do precisely that. The algorithm relies on first predicting the skin using the data from the MAS device, then predicting the fat again using the data from the MAS but also the predicted skin value and lastly the muscle is predicted using the microwave data together with the skin and fat predictions. Data have been collected in phantom experiments, materials that mimick the dielectric properties of human tissues. The algorithm is trained to predict the properties of said phantoms. The results show that the prediction for skin thickness works well, the fat thickness prediction is okay but the muscle prediction struggles. This is partly due to the error from the skin and fat layers are propagated to the muscle layer and partly because the muscle layer is farthest away from the sensor, which makes getting information from that layer harder.

In this paper a standalone microwave device is evaluated for its ability to assess local body composition with the ultimate goal to assess muscle quality. Data have been collected from volunteers who were measured on their thigh using the microwave device and ultrasound. A machine learning algorithm with three stages is designed that utilizes the stacked nature of the tissues in the thigh to predict skin and fat thickness and the cross-sectional area of the rectus femoris muscle. The input to the algorithm is the signal response from the microwave sensor and also the prediction from the previous layers. The ultrasound measurements are used as the ground truth labels for each tissue to train the machine learning models. The measurements were performed with two sensors, where usage of the combined data from both sensors produced the best results for fat and muscle, 0.57 and 0.63 in R ² score, respectively. In the drop analysis, a step where a select proportion of the data is temporarily removed, the identified models showed increased scores with a larger amount of data available indicating the learning of the models improves with more data. Although the results are encouraging more data is ultimately needed to further study the algorithm.

We have developed a new transmission-based, open-ended coaxial probe for assessing vertebrae strength during spinal fusion surgery. The approach exploits the fact that the probes are within the far field of each other implying that the phase varies linearly with respect to propagation distance. Determining the absolute phase is critical for recovering the associated tissue dielectric properties from which bone strength will be determined. Unfortunately, unwanted multi-path signals corrupt the signals at the lower end of the operating frequency range from which our conventional unwrapping strategy depends. Our new approach requires only three measurements within the prime frequency range and can be determined robustly with a minimum of computations. This will be vital to developing a commercial device since the signal levels will be extremely low power requiring longer than usual data acquisition times, which will be mitigated by measuring the data at only a few frequencies. Fast and efficient operation will be critical for clinical success.