Experimental Validation of Microwave Tomographywith the DBIM-TwIST Algorithm for Brain StrokeDetection and Classification

We present an initial experimental validation of a microwave tomography (MWT) prototype for brain stroke detection and classification using the distorted Born iterative method, two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm. The validation study consists of first preparing and characterizing gel phantoms which mimic the structure and the dielectric properties of a simplified brain model with a haemorrhagic or ischemic stroke target. Then, we measure the S-parameters of the phantoms in our experimental prototype and process the scattered signals from 0.5 to 2.5 GHz using the DBIM-TwIST algorithm to estimate the dielectric properties of the reconstruction domain. Ourresultsdemonstratethatweareabletodetectthestroketargetinscenarios where the initial guess of the inverse problem is only an approximation of the true experimental phantom. Moreover, the prototype can differentiate between haemorrhagic and ischemic strokes based on the estimation of their dielectric properties

Effect of Varying Prior Information in Axillary 2D Microwave Tomography

We numerically assess the potential of microwave tomography (MWT) for the detection and dielectric properties estimation of axillary lymph nodes (ALNs), and we study the robustness of our system using prior information with varying levels of accuracy. We adopt a 2-dimensional MWT system with 8 antennas (0.5-2.5 GHz) placed around the axillary region. The reconstruction algorithm implements the distorted Born iterative method. We show that: (i) when accurate prior knowledge of the axillary tissues (fat and muscle) is available, our system successfully detects an ALN; (ii) ±30% error in the prior estimation of fat and muscle dielectric properties does not affect image quality; (iii) ±7mm error in muscle position causes slight artifacts, while ± 14mm error in muscle position affects ALN detection. To the best of our knowledge, this is the first paper in the literature to study the impact of prior information accuracy on detecting an ALN using MWT.info:eu-repo/semantics/publishedVersio

Electromagnetic device for axillary Lymph Node diagnosis

The diagnosis of axillary lymph nodes (ALNs) is fundamental to determine breast cancer staging before making therapeutical decisions. Non-invasive medical imaging techniques are often used to diagnose ALNs, but they lack sensitivity and specificity. This thesis aims to contribute to the development of microwave imaging (MWI) prototype system to detect and diagnose ALNs. The dielectric properties of freshly excised animal lymph nodes (LNs) and human ALNs are measured (0.5-8.5GHz) with the Open-Ended Coaxial-Probe technique. The results show that the relative permittivity of healthy ALNs ranges between 30 and 50 at 4.5GHz, which contrasts well with the surrounding fat tissue, potentially enabling ALN detection with MWI. Additionally, the effects of freezing and defrosting of biological tissue dielectric properties are studied, which is motivated by the possibility of measuring previously frozen and defrosted LNs. The results suggest that measuring defrosted tissues does not affect the estimation of their dielectric properties by more than 9% at 4.5GHz, paving the way to measure previously frozen LN. The measured ALN dielectric properties are used to develop an anatomically realistic axillary phantom. The phantom derives from the segmentation of a thoracic computed-tomography scan, and it is made of polymeric containers filled with appropriate tissue mimicking liquids, representing fat and muscle. Finally, ALN microwave tomography is tested (0.5-2.5GHz) on the developed anthropomorphic phantom, using the distorted Born iterative method. The numerical results show that: (i) prior knowledge on the position of muscle tissue is fundamental for ALN detection; (ii) performing two-step measurements, with the antenna set in two different angular positions, can increase the amount of retrievable information, and enhance imaging results. Regarding experimental results, the proposed system can detect an ALN in different positions in the axillary region, which motivates further studies on ALN MWI