Breast Cancer Detection Using Microwave Low-Frequency Metasurface-Based Techniques - Hernandez Vivanco, Mauricio Enrique (University of Waterloo, 2025)
Breast cancer has become an important global public health issue and is the leading cause of death among women. Early detection plays a pivotal role in the effective treatment of breast cancer. Microwave modalities offer a promising alternative because they allow deep penetration into the breast, resulting in an adequate contrast between the electrical properties of the malignant and benign breast tissue. Microwaves are also a mature technique that is relatively affordable and non-ionizing, thus addressing many of the deficiencies of traditional methods. In the first part of this study, we use computer biomechanics finite element simulation software (FEBio) to simulate the physical compression of anatomically realistic breast phantoms. The phantoms are 3D anatomically realistic and made up of MRI images. The compressed breast models derived from this part of this study will create a realistic scenario for the electromagnetic simulation in CST software. We successfully compressed a set of realistic phantoms, and these accurate compressed models created several natural scenarios to test the microwave technology developed in this investigation. The second part of this work introduces an electromagnetic energy scanning technique with a specific application to detect breast cancer. The technique is based on a field detector probe inspired by the concept of a metasurface composed of an ensemble of electrically small elements where a single element represents the field detector. This novel probe provides a resolution that cannot be matched by either electrically small probes or resonance-based probes that have dimensions comparable to the wavelength. The field emanating from a specific structure, such as a human female breast, can be scanned by the proposed probe to achieve a resolution in the millimeter range while operating in the low-microwave frequency spectrum. The probe was tested numerically, and a prototype was tested experimentally, demonstrating its effectiveness in providing a field resolution of approximately 5 mm. The third part of this thesis introduces a novel metasurface sensor designed to detect breast cancer by operating at sub-microwave frequency. Each cell of the metasurface gives rise to a voltage difference that can be measured at the back of the metasurface. Thus, the metasurface has the ability to generate an impression that relates to the constituents of the breast. Simulations were performed to capture and analyze a collection of images using artificial intelligence methodologies. The findings of this testing stage demonstrated the device's capability to identify anomalies in the breast that may or may not be cancerous. This concept was also extended and validated in an experimental trial; the fabricated device was able to detect a small PEC sphere of 10 mm diameter. Both results, simulation and experimental test, show the potential of this device in successfully detecting breast cancer. This study also explores, in part four, the classification between healthy and unhealthy breast tissue by integrating a microwave probe and a self-organized algorithm. This system collects the reflection coefficients from multiple breast samples where there may or may not be the presence of cancerous tissue. The probe showed remarkable capability to detect the changes in dielectric properties in the breast tissue, and the self-organized algorithm addressed the challenge of classifying this complex data overpassing traditional machine learning methods. We tested the capabilities of this detection system under simulation and experimental validation.
Keywords: Microwaves; Metasurface; Breast Cancer Detection; Artificial Intelligence; Artificial Life; Microwave Imaging; Biomedical application; Self-organized algorithm; Microwave low-frequency scanning; Breast compression; Near-field; Microwave Low-frequency