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

Faculty of Natural and Mathematical Sciences, King’s College London, Strand, London WC2R 2LS, UK
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Sensors 2020, 20(3), 840; https://doi.org/10.3390/s20030840
Received: 29 November 2019 / Revised: 30 January 2020 / Accepted: 31 January 2020 / Published: 4 February 2020
(This article belongs to the Special Issue Microwave Sensing and Imaging)
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. View Full-Text
Keywords: microwave tomography; stroke detection; DBIM microwave tomography; stroke detection; DBIM
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Karadima, O.; Rahman, M.; Sotiriou, I.; Ghavami, N.; Lu, P.; Ahsan, S.; Kosmas, P. Experimental Validation of Microwave Tomography with the DBIM-TwIST Algorithm for Brain Stroke Detection and Classification. Sensors 2020, 20, 840.

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