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Experimental Validation of Microwave Tomographywith the DBIM-TwIST Algorithm for Brain StrokeDetection and
Olympia Karadima1, Mohammed Rahman1, Ioannis Sotiriou1
1Faculty of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UK.
Sensors (Basel, Switzerland)
|February 9, 2020
Summary
This study validates a microwave tomography prototype for detecting and classifying brain strokes. The system successfully identified stroke targets and differentiated between hemorrhagic and ischemic types using dielectric property estimation.
Area of Science:
- Biomedical Engineering
- Medical Imaging
- Electromagnetics
Background:
- Brain stroke detection remains a critical challenge in medical diagnostics.
- Current imaging techniques have limitations in speed, cost, or accessibility.
- Microwave tomography (MWT) offers a potential non-invasive imaging modality.
Purpose of the Study:
- To experimentally validate a microwave tomography (MWT) prototype for brain stroke detection and classification.
- To assess the performance of the distorted Born iterative method, two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm in MWT.
- To differentiate between hemorrhagic and ischemic stroke types based on dielectric properties.
Main Methods:
- Preparation and characterization of gel phantoms mimicking brain tissue and stroke types (hemorrhagic, ischemic).
- Measurement of S-parameters using an experimental MWT prototype across a frequency range of 0.5 to 2.5 GHz.
- Application of the DBIM-TwIST algorithm to process scattered signals and reconstruct dielectric properties.
Main Results:
- Successful detection of stroke targets within the phantoms, even with approximate initial conditions for the inverse problem.
- Accurate estimation of dielectric properties for differentiating between hemorrhagic and ischemic stroke phantoms.
- Demonstration of the MWT prototype's capability for stroke classification.
Conclusions:
- The MWT prototype, utilizing the DBIM-TwIST algorithm, shows promise for non-invasive brain stroke detection.
- The system can effectively distinguish between hemorrhagic and ischemic stroke types.
- Further development could lead to a valuable tool for rapid stroke diagnosis.
