Parallel preconditioners for high order discretizations arising from full system modeling for brain microwave imaging

This paper combines the use of high order finite element methods with parallel preconditioners of domain decomposition type for solving electromagnetic problems arising from brain microwave imaging. The numerical algorithms involved in such complex imaging systems are computationally expensive since they require solving the direct problem of Maxwell's equations several times. Moreover, wave propagation problems in the high frequency regime are challenging because a sufficiently high number of unknowns is required to accurately represent the solution. In order to use these algorithms in practice for brain stroke diagnosis, running time should be reasonable. The method presented in this paper, coupling high order finite elements and parallel preconditioners, makes it possible to reduce the overall computational cost and simulation time while maintaining accuracy

Numerical Modeling and High Speed Parallel Computing: New Perspectives for Tomographic Microwave Imaging for Brain Stroke Detection and Monitoring

This paper deals with microwave tomography for brain stroke imaging using state-of-the-art numerical modeling and massively parallel computing. Microwave tomographic imaging requires the solution of an inverse problem based on a minimization algorithm (e.g. gradient based) with successive solutions of a direct problem such as the accurate modeling of a whole-microwave measurement system. Moreover, a sufficiently high number of unknowns is required to accurately represent the solution. As the system will be used for detecting the brain stroke (ischemic or hemorrhagic) as well as for monitoring during the treatment, running times for the reconstructions should be reasonable. The method used is based on high-order finite elements, parallel preconditioners from the Domain Decomposition method and Domain Specific Language with open source FreeFEM++ solver

Parallel delay multiply and sum algorithm for microwave medical imaging using spark big data framework

Microwave imaging systems are currently being investigated for breast cancer, brain stroke and neurodegenerative disease detection due to their low cost, portable and wearable nature. At present, commonly used radar-based algorithms for microwave imaging are based on the delay and sum algorithm. These algorithms use ultra-wideband signals to reconstruct a 2D image of the targeted object or region. Delay multiply and sum is an extended version of the delay and sum algorithm. However, it is computationally expensive and time-consuming. In this paper, the delay multiply and sum algorithm is parallelised using a big data framework. The algorithm uses the Spark MapReduce programming model to improve its efficiency. The most computational part of the algorithm is pixel value calculation, where signals need to be multiplied in pairs and summed. The proposed algorithm broadcasts the input data and executes it in parallel in a distributed manner. The Spark-based parallel algorithm is compared with sequential and Python multiprocessing library implementation. The experimental results on both a standalone machine and a high-performance cluster show that Spark significantly accelerates the image reconstruction process without affecting its accuracy

3-D printed UWB microwave bodyscope for biomedical measurements

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this letter, a three-dimensional (3-D) printed compact ultrawideband (UWB) extended gap ridge horn (EGRH) antenna designed to be used for biological measurements of the human body is described. The operational frequency covers the microwave band of interest from 0.5 to 3.0 GHz (for an S 11 under -7 dB). The 3-D printed EGRH antenna is dielectrically matched to the permittivity of the human body, and because of its compactness, it can be visualized as a general-purpose microwave probe among the RF biomedical community. The probe has proven its capability as a pass-through propagation sensor for different parts of the human body and as a sensor detecting a 1 cm diameter object placed inside an artificial head phantom.Peer ReviewedPostprint (author's final draft

3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology

Early, preferably prehospital, detection of intracranial bleeding after trauma or stroke would dramatically improve the acute care of these large patient groups. In this paper, we use simulated microwave transmission data to investigate the performance of a machine learning classification algorithm based on subspace distances for the detection of intracranial bleeding. A computational model, consisting of realistic human head models of patients with bleeding, as well as healthy subjects, was inserted in an antenna array model. The Finite-Difference Time-Domain (FDTD) method was then used to generate simulated transmission coefficients between all possible combinations of antenna pairs. These transmission data were used both to train and evaluate the performance of the classification algorithm and to investigate its ability to distinguish patients with versus without intracranial bleeding. We studied how classification results were affected by the number of healthy subjects and patients used to train the algorithm, and in particular, we were interested in investigating how many samples were needed in the training dataset to obtain classification results better than chance. Our results indicated that at least 200 subjects, i.e., 100 each of the healthy subjects and bleeding patients, were needed to obtain classification results consistently better than chance (p < 0.05 using Student\u27s t-test). The results also showed that classification results improved with the number of subjects in the training data. With a sample size that approached 1000 subjects, classifications results characterized as area under the receiver operating curve (AUC) approached 1.0, indicating very high sensitivity and specificity

A low-complexity microwave scanner for cerebrovascular diseases monitoring

This work gathers the pathway from the design to the experimental testing of a microwave imaging prototype to monitor brain stroke in real-time conditions, approaching thus the electromagnetic inverse problem of retrieving a dielectric temporal variation within the head. To this end, it presents a low-complexity device consisting of twentytwo custom-made radiating elements working with a linear imaging algorithm based on distorted Born approximation and a truncated singular value decomposition, able to localize, identify and track the stroke evolution. The system is prototyped using a compact two-ports vector analyzer and electromechanical switching matrix. It is assessed experimentally via a mimicked hemorrhagic condition, demonstrating the system’s capabilities to follow up centimetric confined variations, retrieving 3-D maps of the studied cases in real-time

Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection

In this paper, we present an investigation of different artefact removal methods for ultra-wideband Microwave Imaging (MWI) to evaluate and quantify current methods in a real environment through measurements using an MWI device. The MWI device measures the scattered signals in a multi-bistatic fashion and employs an imaging procedure based on Huygens principle. A simple two-layered phantom mimicking human head tissue is realised, applying a cylindrically shaped inclusion to emulate brain haemorrhage. Detection has been successfully achieved using the superimposition of five transmitter triplet positions, after applying different artefact removal methods, with the inclusion positioned at 0°, 90°, 180°, and 270°. The different artifact removal methods have been proposed for comparison to improve the stroke detection process. To provide a valid comparison between these methods, image quantification metrics are presented. An “ideal/reference” image is used to compare the artefact removal methods. Moreover, the quantification of artefact removal procedures through measurements using MWI device is performed