Paper Info
Title
Experimental Validation of Microwave Tomographywith the DBIM-TwIST Algorithm for Brain StrokeDetection and Classification.
Abstract
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.
Year
DOI
Venue
2020
10.3390/s20030840
SENSORS
Keywords
DocType
Volume
microwave tomography,stroke detection,DBIM
Journal
20
Issue
ISSN
Citations 
3
1424-8220
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Olympia Karadima100.34
Mohammed Rahman200.34
Ioannis Sotiriou301.01
Navid Ghavami400.34
Pan Lu500.68
Syed Ahsan600.34
Panos Kosmas700.34