Microwave Medical Imaging Using Inverse Radon Transform and Quasi-Static Simulator

The features of Microwave Imaging (MI) drive its potential to be widely implemented in different fields. Its high penetration depth, comfortable usage, non-ionizing nature, and low-cost operation have attracted researchers in engineering and medical areas to use MI as an effective tool for medical imaging, remote sensing, and industrial imaging application (e.g., material fabrication and imaging). One of its most relevant and highly required application is to use it as an early-stage medical diagnostic tool for tumor detection as an alternative to conventional medical imaging approaches. MI can overcome the drawbacks of conventional medical imaging techniques like high operation frequency, high energy intensity, and relatively expensive operation (e.g., X-Rays and MRI). Therefore, in this work, we propose a novel technique for implementing MI as an effective imaging tool and implement that in the tumor detection and imaging process. In this study, numerical simulation in a Quasi-static environment is developed to model the imaging process of a body containing either one or more tumors. This model uses a movable and wavelength-independent electrostatic dipole point as a ray-like source to detect the hypothetically formed tumor. The variation of the power (electric field) of the waves generated from the vertically moving localized source that passes through a circular-like rotating object around its center is measured. Accordingly, the projection profile for each point at an imaginary detection line (film) is registered. After that, Filtered Back Projection (FBP) and Inverse Radon Transform (IRT) algorithms are applied to reconstruct a 2D image of the scanned body. In order to reconstruct the body image, the power profile at different source elevations and different body angles are aggregated and processed using inverse Radon transform. Due to different dielectric properties (e.g., permittivity) of the object and the tumor, different measured power profiles are generated and therefore tumors can be detected. Simulations are performed for inhomogeneous and asymmetric structures. Results show the ability of the proposed method to numerically successfully reconstruct good-resolution images and the capability of distinguishing between abnormal and normal tissues. The proposed method can be used as an early-stage and cheap approach for sensing abnormal bodies in human tissues as well as different upcoming applications in different fields