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. Our results demonstrate that we are able to detect the stroke target in scenarios 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.

Stroke is a very frequent disorder and one of the major leading causes of death and disability worldwide. Timely detection of stroke is essential in order to select and perform the correct treatment strategy. Thus, the use of an efficient imaging method for an early diagnosis of this syndrome could result in an increased survival’s rate. Nowadays, microwave imaging (MWI) for brain stroke detection and classification has attracted growing interest due to its non-invasive and non-ionising properties. In this paper, we present a feasibility study with the goal of enhancing MWI for stroke detection using metasurface (MTS) loaded antennas. In particular, three MTS-enhanced antennas integrated in different brain scanners are presented. For the first two antennas, which operate in a coupling medium, we show experimental measurements on an elliptical brain-mimicking gel phantom including cylindrical targets representing the bleeding in haemorrhagic stroke (h-stroke) and the not oxygenated tissue in ischaemic stroke (i-stroke). The reconstructed images and transmission and reflection parameter plots show that the MTS loadings improve the performance of our imaging prototype. Specifically, the signal transmitted across our head model is indeed increased by several dB‘s over the desired frequency range of 0.5–2.0 GHz, and an improvement in the quality of the reconstructed images is shown when the MTS is incorporated in the system. We also present a detailed simulation study on the performance of a new printed square monopole antenna (PSMA) operating in air, enhanced by a MTS superstrate loading. In particular, our previous developed brain scanner operating in an infinite lossy matching medium is compared to two tomographic systems operating in air: an 8-PSMA system and an 8-MTS-enhanced PSMA system. Our results show that our MTS superstrate enhances the antennas’ return loss by around 5 dB and increases the signal difference due to the presence of a blood-mimicking target up to 25 dB, which leads to more accurate reconstructions. In conclusion, MTS structures may be a significant hardware advancement towards the development of functional and ergonomic MWI scanners for stroke detection.

We present an experimental validation of the distorted Born iterative method with the two-step 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.

Over the last two decades, metamaterials (MMs) and metasurfaces (MTSs) have been used to fabricate innovative antenna designs, offering cost-effective solutions compared to conventional radiating systems. This paper investigates the feasibility of combining MM design concepts and imaging techniques to create innovative microwave imaging systems. In particular, we present an experimental study with the aim of enhancing microwave imaging for haemorrhagic stroke detection using a new MTS design. First, we show the improvement in performance for a stand-alone MTS-loaded antenna, by studying its operating characteristics in the near and far fields. Then, we assess the performance of the MTS on the reconstruction results from simulations and measurements on two tissue-mimicking gel-based brain phantoms with a cylindrical target representing the bleeding in haemorrhagic stroke. The brain phantom was immersed inside an imaging tank filled with 90% glycerol matching liquid. To perform the image reconstructions, we used both a Huygens based radar algorithm and a DBIM-TwIST tomography algorithm. Our simulation and measurement results indicate that the proposed MTS design improves target localization and decreases image artefacts for the tomographic algorithm and enables target’s detection through our radar technique, paving the way for a hybrid microwave imaging prototype with MTS enhanced antennas.

We present a preliminary study of microwave head imaging using a three-dimensional (3-D) implementation of the distorted Born iterative method (DBIM). Our aim is to examine the benefits of using the more computationally intensive 3-D implementation in scenarios where limited prior information is available, or when the target occupies an area that is not covered by the imaging array’s transverse planes. We show that, in some cases, the 3-D implementation outperforms its two-dimensional (2-D) counterpart despite the increased number of unknowns for the linear problem at each DBIM iteration. We also discuss how the 3-D algorithm can be implemented efficiently using graphic processing units (GPUs) and validate this implementation with experimental data from a simplified brain phantom. In this work, we have implemented a non-linear microwave imaging approach using DBIM with GPU-accelerated FDTD. Moreover, the paper offers a direct comparison of 2-D and 3-D microwave tomography implementations for head imaging and stroke detection in inhomogenous anatomically complex numerical head phantoms.

This paper presents a new printed square monopole antenna (PSMA) for haemorrhagic stroke detection, enhanced by a metasurface (MTS) superstrate. To show the capabilities of MTS technology in microwave brain imaging, three different tomographic systems are compared in CST Microwave Studio. Our previous developed headband scanner operating in a lossy matching medium is compared to two brain scanners operating in air: a 8-PSMA system and a 8-MTS-enhanced PSMA system. For image reconstruction we used the distorted Born iterative method (DBIM) combined with two-step iterative shrinkage/thresholding (TwIST) algorithm. Our results indicate that a blood-mimicking target placed inside the brain volume of our head model can be detected avoiding the use of a liquid and bulky matching medium. In addition, our MTS superstrate enhances the antennas' reflection coefficient and increases the signal difference due to the presence of the target, which translates into more accurate reconstructions. Thus, MTS technology may be a significant hardware advancement towards the development of functional and ergonomic MWI scanners for haemorrhage detection.