Detection of Simulated Brain Strokes Using Microwave Tomography

Coli, Vanna Lisa; Tournier, Pierre-Henri; Dolean, Victorita; Kanfoud, Ibtissam El; Pichot, Christian; Migliaccio, Claire; Blanc‐Féraud, Laure

doi:10.1109/jerm.2019.2921076

Cited by 53 publications

(15 citation statements)

References 17 publications

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“…To summarize, the inverse scattering results obtained by using the inexact Newton method as a regularization algorithm for the nonlinear equation (5) in some Lebesgue spaces show less presence of over-smoothing in the reconstructed properties of the targets. Naturally, this is an interesting property for all the applications in which an accurate dielectric reconstruction is required.…”

Section: The Novel Variable-exponent Approachmentioning

confidence: 86%

“…The object is illuminated by horn antennas located in S = 8 positions uniformly spaced on a circumference of radius 1.67 m, and, for each view, the scattered field is collected in 241 points uniformly spaced on an arc of 270 • on the same circumference. Data acquired at different frequencies are also available in the range [2][3][4][5][6][7][8][9][10] GHz, with 1 GHz step. The details of the measurement setup can be found in [40].…”

Section: A Reconstruction Examplementioning

confidence: 99%

“…Microwave imaging is attracting an ever-growing interest in several applicative areas, ranging from non-destructive evaluation of civil and geophysical structures to biomedical diagnostics [1][2][3][4][5][6][7][8][9][10][11]. Such an interest is due mainly to the attracting features of microwave techniques.…”

Section: Introductionmentioning

confidence: 99%

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Microwave Imaging by Means of Lebesgue-Space Inversion: An Overview

et al. 2019

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An overview of the recent advancements in the development of microwave imaging procedures based on the exploitation of the regularization theory in Lebesgue spaces is reported in this paper. Such inversion schemes have been found to provide accurate results in several microwave imaging scenarios, thanks to the different geometrical properties that Lebesgue spaces can exhibit with respect to the more classical Hilbert ones. Moreover, the recent extension to the more general case of variable-exponent Lebesgue spaces is also addressed. Experimental results involving reference data are shown for supporting the theoretical description of the approaches.

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Section: The Novel Variable-exponent Approachmentioning

confidence: 86%

Section: A Reconstruction Examplementioning

confidence: 99%

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Microwave Imaging by Means of Lebesgue-Space Inversion: An Overview

et al. 2019

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“…While there are a few experimental studies on detecting a blood-like target using microwave imaging methods [15][16][17]44,45], only a few studies exist that demonstrate experimentally the detection of an ischemic-like area. Our results in this respect are encouraging, as they indicate that a classification of the stroke type is possible with a microwave imaging system without the need of training data [18].…”

Section: Discussionmentioning

confidence: 99%

Experimental Validation of Microwave Tomography with the DBIM-TwIST Algorithm for Brain Stroke Detection and Classification

Karadima

Rahman

Sotiriou

et al. 2020

Sensors

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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.

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“…The most popular medical device being 24 Low Low Low High investigated for stroke diagnosis involves microwave-based stroke sensing. 7,11,14,[20][21][22] This method was first described by Abtahi et al in 2012. 7 All of the studies investigating microwave technology underscored the low-cost, portability, and rapid diagnostic time of this method.…”

Section: Stroke Diagnosis Using Microwavesmentioning

confidence: 99%

A systematic review of next-generation point-of-care stroke diagnostic technologies

Shahrestani

Wishart²,

Han³

et al. 2021

Neurosurgical Focus

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OBJECTIVE Stroke is a leading cause of morbidity and mortality. Current diagnostic modalities include CT and MRI. Over the last decade, novel technologies to facilitate stroke diagnosis, with the hope of shortening time to treatment and reducing rates of morbidity and mortality, have been developed. The authors conducted a systematic review to identify studies reporting on next-generation point-of-care stroke diagnostic technologies described within the last decade. METHODS A systematic review was performed according to PRISMA guidelines to identify studies reporting noninvasive stroke diagnostics. The QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) tool was utilized to assess risk of bias. PubMed, Web of Science, and Scopus databases were utilized. Primary outcomes assessed included accuracy and timing compared with standard imaging, potential risks or complications, potential limitations, cost of the technology, size/portability, and range/size of detection. RESULTS Of the 2646 reviewed articles, 19 studies met the inclusion criteria and included the following modalities of noninvasive stoke detection: microwave technology (6 studies, 31.6%), electroencephalography (EEG; 4 studies, 21.1%), ultrasonography (3 studies, 15.8%), near-infrared spectroscopy (NIRS; 2 studies, 10.5%), portable MRI devices (2 studies, 10.5%), volumetric impedance phase-shift spectroscopy (VIPS; 1 study, 5.3%), and eddy current damping (1 study, 5.3%). Notable medical devices that accurately predicted stroke in this review were EEG-based diagnosis, with a maximum sensitivity of 91.7% for predicting a stroke, microwave-based diagnosis, with an area under the receiver operating characteristic curve (AUC) of 0.88 for differentiating ischemic stroke and intracerebral hemorrhage (ICH), ultrasound with an AUC of 0.92, VIPS with an AUC of 0.93, and portable MRI with a diagnostic accuracy similar to that of traditional MRI. NIRS offers significant potential for more superficially located hemorrhage but is limited in detecting deep-seated ICH (2.5-cm scanning depth). CONCLUSIONS As technology and computational resources have advanced, several novel point-of-care medical devices show promise in facilitating rapid stroke diagnosis, with the potential for improving time to treatment and informing prehospital stroke triage.

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Detection of Simulated Brain Strokes Using Microwave Tomography

Cited by 53 publications

References 17 publications

Microwave Imaging by Means of Lebesgue-Space Inversion: An Overview

Microwave Imaging by Means of Lebesgue-Space Inversion: An Overview

Experimental Validation of Microwave Tomography with the DBIM-TwIST Algorithm for Brain Stroke Detection and Classification

A systematic review of next-generation point-of-care stroke diagnostic technologies

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