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Adaptive Clustering Distorted Born Iterative Method for Microwave Brain Tomography With Stroke Detection and

Lei Guo, Mojtaba Khosravi-Farsani, Anthony Stancombe

    IEEE Transactions on Bio-Medical Engineering
    |October 25, 2021
    PubMed
    Summary

    A new adaptive clustering method improves brain imaging for stroke detection. This adaptive clustering distorted Born iterative method (AC-DBIM) offers better accuracy in reconstructing electrical properties compared to conventional techniques.

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    Area of Science:

    • Electromagnetic Imaging
    • Biomedical Engineering
    • Medical Physics

    Background:

    • Accurate brain imaging is crucial for diagnosing neurological conditions like stroke.
    • Conventional methods for reconstructing electrical properties (EPs) in brain imaging face limitations in accuracy and resolution.
    • Distorted Born iterative method (DBIM) is a common technique, but improvements are needed for clinical applications.

    Purpose of the Study:

    • To introduce and evaluate a modified distorted Born iterative method (AC-DBIM) for enhanced brain imaging.
    • To improve stroke detection and classification through more accurate reconstruction of electrical properties.
    • To compare the performance of AC-DBIM against conventional methods like DBIM, MR-CSI, and SL-CSI.

    Main Methods:

    • Developed an adaptive clustering DBIM (AC-DBIM) incorporating clustering of reconstructed EPs.
    • Required prior information included the number of materials and their approximate dielectric properties.
    • Evaluated AC-DBIM using 2D and 3D simulations on head phantoms and numerical models, and experimental assessments with a clinical electromagnetic head scanner.

    Main Results:

    • AC-DBIM demonstrated superior performance compared to conventional DBIM, MR-CSI, and SL-CSI.
    • Significant improvements were observed in the size and shape reconstruction of abnormalities.
    • AC-DBIM achieved notable reductions in errors and standard deviation for reconstructed permittivity (εr) and conductivity (σ).

    Conclusions:

    • The proposed AC-DBIM algorithm offers a significant advancement for brain imaging, particularly for stroke detection and classification.
    • AC-DBIM provides enhanced accuracy in reconstructing electrical properties, leading to better diagnostic capabilities.
    • The method shows promise for real-world clinical applications, outperforming existing conventional techniques.