1,720,982 research outputs found
EM Device for Cerebrovascular Diseases Imaging
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Detection and Monitoring of Brain Stroke Onsets by an Ad-hoc Double Stage Delay-Multiply-And-Sum (DS-DMAS) Algorithm
Full text linkThis paper presents an ad hoc Double-Stage Multiply-Delay-and-Sum (DS-DMAS) confocal algorithm with a pre-calibration stage and simplified wave propagation compensation adapted for microwave-imaging-based detection and monitoring of brain stroke onsets. We numerically assesses the algorithm using mimicked clinical scenarios of detection and follow-up, including a setup with a multiview 22-antennas system operating from 0.8 to 1.8 GHz and both anthropomorphic homogeneous and multi-tissue head models. The results demonstrate the capability to detect and monitor the pathology, providing 3D intensity power maps highlighting the evolving stroke-affected areas
Real-time 3D microwave tomography of brain stroke status using low-computing demand
No full textThis paper approaches the medical problem of the after-onset monitoring of a brain stroke via a real-time linear imaging algorithm and a low-complexity microwave scanner. This procedure allows using low computing requirements for tracking physical pathology changes, such as stroke shape evolution and he partial typology variation of the infarcted zones, both significant medical issues. The system consists of a 22-antenna device,
and the imaging algorithm uses a differential single-frequency approach. It exploits a pair of measured scattering matrices taken at two different instants, the Born approximation, and the truncated singular value decomposition, to form in-time 3D tomographic dielectric contrast variation maps in real-time using a stand-alone low-capacity device without needing a graphics processing unit. The results confirm the continuous stroke followup
capabilities of the system, with the possibility to track both the shape and type transformations (hemorrhage and ischemia), even in mimicked complex clinical scenarios
Realistic Numerical Modelling for 3-D brain stroke monitoring
Full text linkThis paper aims to provide a realistic 3-D modelling framework of a real-world microwave imaging system for brain monitoring that mimics pre-assessment experimental clinical scenarios and lab setups. The model considers an anthropomorphic adult head with multiple tissues, a hemorrhagic brain stroke and a detailed prototype of a modular microwave antennas helmet. The set of antennas detect changes in the permittivity of biological tissues and then imaging reconstruction algorithms generates a 3-D representation of stroke using EM fields and scattering data generated by a full-wave numerical simulation. As results, it is presented a reconstruction of onset stroke in the white matter area of the brain using a TSVD algorithm and the born approximation for imaging
Electromagnetic Virtual Prototyping of a Realistic 3-D Microwave Scanner for Brain Stroke Imaging
Full text linkTowards a preclinical prototype for diagnostic and monitoring of cerebral pathologies, here we present the 3D electromagnetic (EM) virtual prototyping of different clinical scenarios as an instrument for studying the interaction of biological tissues with EM waves, for designing a microwave brain imaging scanner and for generating a set EM fields usually required by imaging algorithms. We employ a full- wave modelling, which uses a Method of Moment (MoM) solver with high order basis functions and includes frequency variable electrical parameters for each component. The model of the microwave imaging system consists of 24-element conformal antennas, an anthropomorphic adult human head, and a spherical shape blood-filled as stroke. Here, the simulated system and data are tested applying an imaging algorithm based on Truncated Singular Value Decomposition (TSVD) and Born approximation, but they can be combined with other microwave imaging algorithms
Multi-shot Calibration Technique for Microwave Imaging Systems
Full text linkThis 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
Brick Shaped Antenna Module for Microwave Brain Imaging Systems
Full text linkIn 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
Microwave Imaging Evaluation of Prior Structural Information on the Inversion-Kernel Building Apply to a Brain Stroke Monitoring Scenario
Full text linkThis work investigates the impact of prior structural information on the inversion kernel used to follow up on a brain stroke condition. To that end, we perform a numerical study that mimics an intracranial hemorrhage and aims to retrieve the morphological evolution of the stroke-affected area between different time instants via direct inversion based on the Born Approximation and the truncated singular value decomposition. Then, we consider different operators, imaging kernels, adding tissue shape information, and evaluating the imaging retrieval performance via the structural similarity index, the dice similarity coefficient, the normalized Hausdorff distance, and a sizebased metric, similarity metrics. The results confirm that more apriori information improves overall performance; however, more importantly, they show that even with approximated kernels, less information, and a more realistic clinical scenario, the imaging might perform well enough as a medical indication
Hybrid imaging kernel calibration applied on microwave scanner for brain stroke monitoring
Full text linkThis 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
Complex-Valued DNN for Broadband Dielectric Characterization of Dispersive Lossy Materials
Full text linkThis paper presents a broadband dielectric characterization method based on a Complex-Valued Deep Neural
Network (CVNN) that allows the retrieval of permittivity and
conductivity of dispersive lossy materials using ad-hoc setups. To
validate the method, we numerically tested it employing a partially filled custom-made double-ridge waveguide setup, working
from 0.95 to 4.2 GHz. Moreover, we include a feature importance
analysis using agnostic explainable-AI (XAI) techniques. The
results demonstrate the flexibility and the retrieval capabilities
of the method, as well as the advantages and drawbacks in
comparison with traditional techniques. We publicly release the
dataset and codes to support further research
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