Introduction: Electromagnetic imaging is an emerging technology which promises to provide a mobile, and rapid neuroimaging modality for pre-hospital and bedside evaluation of stroke patients based on the dielectric properties of the tissue. It is now possible due to technological advancements in materials, antennae design and manufacture, rapid portable computing power and network analyses and development of processing algorithms for image reconstruction. The purpose of this report is to introduce images from a novel, portable electromagnetic scanner being trialed for bedside and mobile imaging of ischaemic and haemorrhagic stroke. Methods: A prospective convenience study enrolled patients (January 2020 to August 2020) with known stroke to have brain electromagnetic imaging, in addition to usual imaging and medical care. The images are obtained by processing signals from encircling transceiver antennae which emit and detect low energy signals in the microwave frequency spectrum between 0.5 and 2.0 GHz. The purpose of the study was to refine the imaging algorithms. Results: Examples are presented of haemorrhagic and ischaemic stroke and comparison is made with CT, perfusion and MRI T2 FAIR sequence images. Conclusion: Due to speed of imaging, size and mobility of the device and negligible environmental risks, development of electromagnetic scanning scanner provides a promising additional modality for mobile and bedside neuroimaging.
Hepatic steatosis is a disorder with high prevalence among obese people. Traditional imaging modalities are more common in hepatic steatosis diagnosis, but they are not suitable for monitoring or treatment evaluation. This study aims at developing a new technique suitable for electromagnetic (EM) tool in the microwave band to differentiate steatotic from nonsteatotic liver. A differential permittivity estimation method for hepatic steatosis detection is proposed. First, the effective permittivity of the right side of the torso is estimated based on the phase difference of EM waves traveling along symmetric paths within the torso. Then, permittivity modeling and statistical frequency selection are performed to model the estimated values and to extract reliable frequency samples. Finally, the percentage of the difference between the permittivity of the left and right sides of the torso is calculated over the selected samples. The effectiveness of the proposed method is validated using simulated signals and phantom measurements. The analyzed results reveal higher contrast between the average permittivity of the left and right sides of the torso for cases with hepatic steatosis (average contrast of 29.2%) compared to those with healthy liver (average contrast of 7.9%). The proposed method can differentiate between steatotic and nonsteatotic liver. It is suitable for clinical applications due to its robustness to unwanted noise and interferences, as well as errors in placement of sensors. The results verify the potential of EM devices, which could overcome shortcomings of traditional imaging techniques by being safe, cost-effective, and portable.
There is a significant demand for fast and accurate electromagnetic (EM) imaging of stroke in emergency situations. This article presents a method for encoding the raw S-parameters from the Cartesian matrix to polar grid coordinates with weighting coefficients based on the receiver antenna spatial sensitivity. The polar sensitivity encoding (PSE) scheme is based on the fact that the receiver sensitivity generally has an encoding effect and, in this case, it is applied during the transformation of S-matrices to polar grid, which is geometrically congruent with the shape of the head. The PSE scheme alleviates the need for highly accurate and intricate forward and inverse EM field solvers and mitigates the introduction of numerical errors in addition to the unavoidable experimental uncertainties. The simulation and experimental results demonstrate that the PSE method is robust to head shifts up to about 5 mm and accurate in localizing strokes in less than a second.
A wearable electromagnetic belt system for the detection of hepatic steatosis (lipid accumulation within the major liver cells, hepatocytes), is proposed. To satisfy the requirements of the belt system, an array of body matched antennas is designed. The belt, which goes around the lower chest and over the liver, requires compact, wideband, unidirectional antennas that operate at low microwave frequencies. To avoid using conventional bulky reflector structures, the designed antenna utilizes the loop-dipole combination concept. To enhance electromagnetic wave penetration, the antenna is designed to match the human body. Thus, thanks to the high dielectric loading from the human body, the dipole element of the antenna is easily miniaturized. Since the same principle does not apply on the loop structure, meandered arc-shapes are employed to increase the effective electrical length of the loop. The final antenna design has a measured wide operating bandwidth of 0.58-1.6 GHz with a compact size of $0.096\times 0.048 \times 0.048\lambda ^{3}$ . The proposed structure is effective in irradiating the torso, where the signal can reach center of the liver at a depth of 90 mm, with 64% of the peak radiated power. An electromagnetic belt is built using twelve elements of the designed antennas. The belt is then tested on a 3D printed torso phantom that includes models of the lungs and liver. Due to close dielectric properties of the other tissues inside the torso, these are represented using an average tissue mimicking mixture with permittivity of 46. Measured data are analyzed using multivariate energy statistics method. A peak measured dissimilarity of 15.1% between steatotic and healthy liver is attained. These initial tests and obtained results indicate the potential of the proposed system as a method to diagnose hepatic steatosis.
A brain anomaly localization algorithm in an unsupervised machine learning (ML) framework is presented for electromagnetic brain imaging. The method is based on expected value estimation and takes the advantage of the highly symmetrical human brain. The algorithm processes signals collected from pairs of antennas that are positioned symmetrically around the head, discretizes the imaging domain into pixels, and computes the statistical fields between the antennas on the left and right sides of the head. Then, it concatenates their intensities along the axis normal to the imaging domain to compute the expected value for every pixel. The computed expected values are merged into a matrix containing expected values for all pixels. Pixels with higher intensity show the likelihood of an anomaly being present at that location. The assumption on brain symmetry from the electromagnetic perspective was tested on healthy volunteers using a 14-element array system with a working frequency band of 0.5 - 2.0 GHz. The obtained average similarity is 92% and it confirms the validity of the assumption. The same system is used to test the algorithm on different scenarios in simulations and experiments using realistic 3D head phantoms designed based on MRIs of real patients. The imaging results demonstrate the capability of the proposed algorithm to localize bleeding and estimate its size with less than 10% error in less than a minute, which makes it suitable for real-time use in emergency stroke scenarios.
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