Discretization Error Analysis in the Contrast Source Inversion Algorithm

This paper describes the use of the contrast source inversion method combined with the finite element method for the numerical solution of 3-D microwave inversion problems. In particular, this work is focused on the discretization of the involved physical vector quantities, analyzing the impact of the chosen discretization on the solution process with the goal of optimizing the implemented algorithm in terms of accuracy, memory requirements and computational cost

Multi-shot Calibration Technique for Microwave Imaging Systems

This paper proposes a novel “multi-shot” calibration technique that reduces imaging microwave reconstructions artifacts, compensating for uncontrolled variations during the measuring process and later propagated in the inversion. The calibration combines different consecutive sets of measured data with simulated ones in a post-processing stage, providing benefits without the need for additional experimental reference calibrations. The proposed scheme is tested experimentally in a non-trivial scenario. A microwave scanner images an early-stage hemorrhagic stroke in the left parietal lobe, applying a differential imaging algorithm based on the truncated singular value decomposition. Though, the proposed mechanisms can be used for other microwave imaging devices. The results reveal that the calibration procedure improves the quality of the retrieved images compared to the non-calibrated approach, cleaning the images and making the interpretation of imaged contrast variation easier

Efficient Data Generation for Stroke Classification via Multilayer Perceptron

The aim of this paper is to overcome one of the main problems of machine learning when it faces the medical world: the need of a large amount of data. Through the distorted Born approximation, the scattering parameters and the dielectric contrast in the domain of interest are linked by a linearized integral operator. This method allows to generate a large dataset in a short time. In this work, machine learning is exploited to classify brain stroke presence, typology and position. The classifier model is based on the multilayer perceptron algorithm and it is used firstly for validation and then with a testing set composed by full-wave simulations. In both cases, the model reaches very high level of accuracy

Model-based data generation for support vector machine stroke classification

This paper presents a new and efficient method to generate a dataset for brain stroke classification. Exploiting the Born approximation, it derives scattering parameters at antennas locations in a 3-D scenario through a linear integral operator. This technique allows to create a large amount of data in a short time, if compared with the full-wave simulations or measurements. Then, the support vector machine is used to create the classifier model, based on training set data with a supervised method and to classify the test set. The dataset is composed by 9 classes, differentiated for presence, typology and position of the stroke. The algorithm is able to classify the test set with a high accuracy

Brain Stroke Classification via Machine Learning Algorithms Trained with a Linearized Scattering Operator

This paper proposes an efficient and fast method to create large datasets for machine learning algorithms applied to brain stroke classification via microwave imaging systems. The proposed method is based on the distorted Born approximation and linearization of the scattering operator, in order to minimize the time to generate the large datasets needed to train the machine learning algorithms. The method is then applied to a microwave imaging system, which consists of twenty-four antennas conformal to the upper part of the head, realized with a 3D anthropomorphic multi-tissue model. Each antenna acts as a transmitter and receiver, and the working frequency is 1 GHz. The data are elaborated with three machine learning algorithms: support vector machine, multilayer perceptron, and k-nearest neighbours, comparing their performance. All classifiers can identify the presence or absence of the stroke, the kind of stroke (haemorrhagic or ischemic), and its position within the brain. The trained algorithms were tested with datasets generated via full-wave simulations of the overall system, considering also slightly modified antennas and limiting the data acquisition to amplitude only. The obtained results are promising for a possible real-time brain stroke classification

Multi-Antenna System for In-Line Food Imaging at Microwave Frequencies

This work presents the design and numerical assessment of a novel microwave imaging (MWI) system, capable of providing a full 3-D image of food/beverage products content in order to disclose the possible presence of physical contaminants, such as plastic fragments. The system here presented exploits the dielectric contrast between the food content and possible intrusions at microwave frequencies; it is based on an antenna array architecture inspecting the items in motion along a conveyor belt without interrupting the production process. The inversion problem is solved by means of linearization, assuming the viability of the Born approximation thanks to the localized intrusions, and regularization, based on the singular value decomposition of the discretized scattering operator. Furthermore, an algorithm, to balance the illumination of the considered scenario due to the nonuniform radiation of the employed antennas, is presented to enhance imaging. The system is first assessed considering an ideal case and then extended to a more realistic approach, for two different kinds of food products, with completely different dielectric properties and considering the performance of existing instrumentation for the purpose. The obtained results lay the foundations for the realization of an actual prototype

Hybrid imaging kernel calibration applied on microwave scanner for brain stroke monitoring

This paper validates a calibration procedure applied on a microwave imaging (MWI) kernel based on the combination of pre-computed simulated data and available S-parameters measurements. The assessed technique compensates for the image degradation caused by mild and non-modeled features of the imaging device, such as the unavoidable manufacturing discrepancies in the antenna array. The testing considers a synthetically mimicked experimental scenario of a hemorrhagic stroke condition and a realistic scanner prototype. This approach allows a thorough comparative assessment of the calibration effect on the electric field estimation used by the MWI algorithm, hardly achievable with measurements. The results show the capability of the calibration procedure to reduce the retrieved images’ distortions and artifacts compared to the non-calibrated approach, being an essential milestone toward its application in real-life scenarios

Brick Shaped Antenna Module for Microwave Brain Imaging Systems

In this letter, we describe and validate a microwave antenna designed for an imaging device for the diagnosis and monitoring of cerebrovascular pathologies. The antenna consists of a printed monopole immersed in a parallelepipedic block of semiflexible material with custom-permittivity, which allows to avoid the use of liquid coupling media and enables a simple array arrangement. The “brick” is built with a mixture of urethane rubber and graphite powder. The -10 dB frequency band of the antenna is 800 MHz-1.2 GHz, in agreement with the device requirements. The designed brick antenna is assessed in terms of power penetration, reflection, and transmission coefficients. To show the performance of the antenna in the relevant application scenario, an experiment has been carried out on an anthropomorphic head phantom, measuring the differential signals between healthy state and hemorrhagic stroke mimicking condition for different antennas positions

Brain Stroke Microwave Imaging via an Efficient Implementation of the CSI-FEM Algorithm

Microwave imaging of the human head for stroke detection is demonstrated using the finite-element contrast source inversion method with enhanced discretization of the contrastsource variable. The linear basis functions used in the new discretization lead to a simple implementation of higher accuracy compared to discretizations wherein the contrast source variable is assumed to be constant over each tetrahedron of the 3D finite element mesh. These advantages are particularly important for stroke imaging because of the highly inhomogeneous nature of the human head. Results using synthetic data obtained from a realistic numerical model of the head show promise for stroke detection