Experimental Validation of Microwave Tomography with the DBIM-TwIST Algorithm for Brain Stroke Detection and Classification
Olympia Karadima, Mohammed Rahman, Ioannis Sotiriou, Navid Ghavami, Pan Lu, Syed Ahsan, Panagiotis Kosmas
Abstract
We present an initial experimental validation of a microwave tomography (MWT) prototypefor brain stroke detection and classification using the distorted Born iterative method, two-stepiterative shrinkage thresholding (DBIM-TwIST) algorithm. The validation study consists of firstpreparing and characterizing gel phantoms which mimic the structure and the dielectric propertiesof a simplified brain model with a haemorrhagic or ischemic stroke target. Then, we measure theS-parameters of the phantoms in our experimental prototype and process the scattered signals from 0.5to 2.5 GHz using the DBIM-TwIST algorithm to estimate the dielectric properties of the reconstructiondomain. Our results demonstrate that we are able to detect the stroke target in scenarios where theinitial 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 theestimation of their dielectric properties.