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

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

Comparison of 2-D and 3-D DBIM-TwIST for Brain Stroke Detection and Differentiation

This work presents preliminary results from a three-dimensional (3-D) iterative microwave imaging algorithm vs. its previous two-dimensional (2-D) implementation for detecting brain stroke. The imaging algorithm is based on the distorted Born iterative method (DBIM) combined with the two-step iterative shrinkage thresholding (TwIST) method. Our test scenarios are based on simplified phantoms with hemorrhagic and ischemic stroke targets, which are placed inside our previously developed microwave imaging prototype and are simulated using CST Microwave Studio. Our results demonstrate that both 2-D and 3-D implementations can detect and differentiate a stroke target placed at the same height (but off-centre) with the antenna array. Moreover, the target’s dielectric properties are estimated more accurately with the 3-D algorithm

Differentiation of brain stroke type by using microwave-based machine learning classification

Brain stroke is an emergency condition that is caused either by a blocked or a burst vessel (ischemic or hemorrhagic stroke, respectively), resulting into abnormal blood supply into the affected area, with severe and sometimes deadly consequences. Early diagnosis of the stroke is vital, as the time that passes from the offset of the symptoms is strictly correlated with the survival of the patient and the treatment success. Concurrently, it is also essential to successfully identify the type of the stroke as treating a hemorrhagic stroke (h-stroke) as an ischemic (i-stroke) stroke could be lethal for the patient [1] . Therefore, there is an increased need for a portable and low-cost diagnostic method that will detect and differentiate the type of the brain stroke as early as possible

Image Improvement Through Metamaterial Technology for Brain Stroke Detection

In this paper we investigate the capabilities of metamaterials technology to enhance the quality of reconstructed images for the problem of brain stroke detection. We integrate the metamaterial in our headband system for brain imaging in CST, and evaluate the reconstructed images of the head model that is placed inside the microwave tomographic head system for the cases with and without the incorporated metamaterial. For image reconstruction we apply the distorted Born iterative method (DBIM) combined with two-step iterative shrinkage/thresholding (TwIST) algorithm. Our results indicate that the use of our metamaterial can increase the signal difference due to the presence of a blood target, which translates into more accurate reconstructions of the target

Experimental comparison of two printed monopole antenna designs for microwave tomography

This paper presents an initial experimental comparison of two custom-made printed monopole antenna arrays for microwave imaging (MWI). Data is obtained by using the MWI system in the presence of regularly shaped gel phantoms mimicking the dielectric properties of average brain and blood. The antenna array and phantom are immersed inside a 90% glycerol, 10% water mixture. Our in-house two-dimensional (2D) imaging algorithm is applied to the acquired data to test and validate the sensitivity of the system, and the value of using multiple frequency reconstructions enabled by wideband antenna operation is demonstrated

Experimental Validation of the DBIM-TwIST Algorithm for Brain Stroke Detection and Differentiation Using a Multi-Layered Anatomically Complex Head Phantom

We present an experimental validation of the distorted Born iterative method with the twostep iterative shrinkage thresholding (DBIM-TwIST) algorithm for the problem of brain stroke detection and differentiation, using an anatomically accurate, multi-layer head phantom. To this end, we have developed a gelatine-based, anatomically complex head phantom which mimics various brain tissues and also includes a target mimicking hemorrhagic or ischemic stroke. We simulated the model and setup using CST Microwave Studio and then used our experimental imaging setup to collect numerical and measured data, respectively. We then used our DBIM-TwIST algorithm to reconstruct the dielectric properties of the imaging domain for both simulated and measured data. Results from our CST simulations showed that we are able to locate and reconstruct the permittivity of different stroke targets using an approximate initial guess. Our experimental results demonstrated the potential and challenges for successful detection and differentiation of the stroke targets