Detection of Biological Tissue Anomalies Using Low-Frequency Electromagnetic Fields

Abstract

This PhD thesis presents a novel biomedical imaging modality—proposed and developed for the first time—for the detection of breast cancer using low-frequency electromagnetic (EM) fields. The core principle stems from the fact that the penetration depth of EM waves into biological tissues is inversely proportional to their operating frequency. Unlike conventional high-frequency imaging techniques, this approach leverages sub-GHz frequencies (hundreds of MHz), which offer significantly deeper tissue penetration, making them particularly suitable for imaging dense breast tissues (BI-RADS categories C and D), where conventional X-ray mammography fails.

Operating at low frequencies introduces critical challenges in designing radio-frequency (RF) components that are compact, human-compatible, and suitable for clinical deployment. To address this, a novel low-frequency metasurface-based film antenna—conceptually analogous to traditional X-ray films—has been developed. This metasurface sensor effectively captures scattered EM fields after interaction with biological tissues, enabling high-fidelity imaging while operating within a non-ionizing and biologically safe frequency range.

The proposed system is cost-effective and portable, with strong potential for widespread deployment in low-resource settings where access to magnetic resonance imaging (MRI) is limited. Unlike MRI, which is expensive and not readily available, or ultrasound, which is prone to operator-dependent errors, this technique enables consistent and repeatable screening.

Also, this work investigates the impact of various EM sources on image resolution and contrast. It is shown that magnetically enhanced sources significantly improve field-tissue interaction, thereby increasing sensitivity to early-stage tumorous anomalies. Advanced post-processing algorithms, including differentiation techniques and both supervised and unsupervised machine learning models, were implemented to enhance image quality and minimize diagnostic errors, further improving the system’s diagnostic performance.

The methodology has been rigorously validated through both numerical simulations and experimental studies. Multiple iterations of the transmitter antennas and metasurface sensors have been developed, optimized, and evaluated throughout the course of the research. The final system demonstrates high accuracy in detecting early-stage abnormalities.

Moreover, this thesis introduces a new low-frequency tomography method, also for the first time, that reconstructs images of internal tissues by modeling the X-ray-like behavior of localized, electrically small transmitters and receivers. A novel mathematical framework has been proposed and implemented using Radon transform techniques, enabling accurate spatial reconstruction of the object under test.