Abstract
We present an experimental validation of the distorted Born iterative method with the two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm for the problem of brain stroke detection and differentiation, using an anatomically accurate, multi-layer head phantom. To this end, we have developed a gelatine-based, anatomically complex head phantom which mimics various brain tissues and also includes a target mimicking hemorrhagic or ischemic stroke. We simulated the model and setup using CST Microwave Studio and then used our experimental imaging setup to collect numerical and measured data, respectively. We then used our DBIM-TwIST algorithm to reconstruct the dielectric properties of the imaging domain for both simulated and measured data. Results from our CST simulations showed that we are able to locate and reconstruct the permittivity of different stroke targets using an approximate initial guess. Our experimental results demonstrated the potential and challenges for successful detection and differentiation of the stroke targets.
Highlights
Cerebrovascular accidents are among the leading causes of death and disability worldwide [1]
Magnetic resonance imaging (MRI) and computed tomography (CT) scans are widely used as acute-care imaging methods for stroke detection [3]
We have validated experimentally our two-dimensional (2-D) DBIMTwIST algorithm and imaging system with simplified head phantoms, for which we showed our method’s potential to differentiate stoke types [18]
Summary
Cerebrovascular accidents (brain strokes) are among the leading causes of death and disability worldwide [1]. Brain strokes are caused by a ruptured (hemorrhagic) or a blocked (ischemic) vessel. Magnetic resonance imaging (MRI) and computed tomography (CT) scans are widely used as acute-care imaging methods for stroke detection [3]. Their use for pre-clinical diagnosis or continuous monitoring is limited by cost, size and mobility [4]. Microwave imaging (MWI) [6] is among the candidates to satisfy these requirements towards detecting and monitoring brain stroke in the prehospital or post-acute stage
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