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    Area of Science:

    • Medical Imaging
    • Ultrasound Technology
    • Signal Processing

    Background:

    • Acoustic clutter in ultrasound imaging causes artifacts, degrading image quality.
    • Aperture Domain Model Image Reconstruction (ADMIRE) effectively mitigates these artifacts but suffers from long run-times.
    • Existing methods for accelerating ADMIRE are limited.

    Purpose of the Study:

    • To evaluate reduced model methods for accelerating ADMIRE.
    • To assess the impact of short-time Fourier transform (STFT) window overlap on ADMIRE performance.
    • To identify the most efficient ADMIRE implementation for clinical use.

    Main Methods:

    • Analysis of reduced models including Gram-Schmidt orthonormalization (GSO), singular value decomposition (SVD), and independent component analysis (ICA).
    • Evaluation of ADMIRE performance using full, GSO, SVD, and ICA-fourth-order blind identification (ICA-FOBI) models.
    • Testing with simulated, phantom, and in vivo liver ultrasound data.

    Main Results:

    • The ICA-FOBI reduced model demonstrated the most promise for accelerating ADMIRE.
    • ADMIRE with ICA-FOBI showed comparable contrast improvement to the full model in vivo liver data.
    • Reduced models decreased ADMIRE's computational complexity by over three orders of magnitude.
    • A 50% STFT window overlap reduced run-time by over an order of magnitude with minimal image quality loss.

    Conclusions:

    • The ICA-FOBI model combined with a 50% STFT window overlap significantly enhances ADMIRE's computational efficiency.
    • This optimized ADMIRE approach offers a practical solution for reducing ultrasound artifacts in real-time applications.
    • Further investigation into reduced model methods can lead to more accessible and efficient ultrasound imaging.