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    Area of Science:

    • Medical Imaging
    • Electromagnetics
    • Computational Science

    Background:

    • Microwave imaging (MWI) offers a non-ionizing approach for medical diagnostics.
    • Compressive sensing (CS) techniques can enhance MWI by reducing data requirements and improving image quality.
    • Iterative reconstruction methods are crucial for accurate MWI data processing.

    Purpose of the Study:

    • To develop and evaluate a novel multifrequency MWI technique.
    • To integrate compressive sensing (CS) with the distorted Born iterative method.
    • To enhance MWI reconstruction quality and reduce data acquisition needs.

    Main Methods:

    • A modified iterative shrinkage thresholding algorithm (ISTA) was developed.
    • An automatic and adaptive selection of multithreshold values was implemented.
    • The approach was tested on 2-D numerical breast phantoms and 3-D simulations.

    Main Results:

    • The adaptive multithreshold ISTA outperformed standard thresholding in 2-D phantom reconstructions.
    • Successful 3-D simulation results were achieved despite forward model mismatch.
    • Modified hard constraints improved reconstruction quality in the inversion procedure.

    Conclusions:

    • The proposed adaptive multifrequency MWI technique shows significant potential for medical applications.
    • The integration of CS and iterative methods offers a powerful approach for MWI.
    • Further development could lead to improved diagnostic capabilities in MWI.