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    Area of Science:

    • Medical imaging
    • Ultrasound technology
    • Signal processing

    Background:

    • Traditional Minimum Variance (MV) adaptive beamforming algorithms face challenges in achieving high resolution, contrast, and real-time performance.
    • Existing methods often struggle with robustness and computational efficiency for advanced imaging applications.

    Purpose of the Study:

    • To develop novel Multi-Operator Minimum Variance (MOMV) adaptive beamforming algorithms for enhanced medical imaging.
    • To design a GPU-based parallel acceleration framework to enable real-time implementation of MOMV algorithms.
    • To evaluate the performance improvements in resolution, contrast, and robustness offered by MOMV beamforming.

    Main Methods:

    • Introduction of a multi-operator optimization framework for MV adaptive beamforming.
    • Derivation of MOMV adaptive beamforming algorithms based on the framework.
    • Development of a GPU-based parallel acceleration strategy, including coarse-grained and fine-grained parallelization.
    • Exploration of GPU computing resource and memory access strategies for optimization.
    • Quantitative simulations and qualitative in vivo experiments to assess imaging performance.

    Main Results:

    • MOMV adaptive beamforming algorithms significantly enhance imaging performance compared to conventional MV algorithms.
    • Achieved improvements include higher resolution, better contrast, and increased robustness.
    • Real-time imaging capabilities were realized through GPU acceleration, yielding speedups of thousands.
    • A variant of MOMV beamforming without specific optimization operators demonstrated a higher frame rate with minimal image quality compromise.

    Conclusions:

    • The proposed MOMV adaptive beamforming algorithms offer superior imaging quality and real-time performance.
    • The GPU-based acceleration framework is effective in achieving high frame rates for advanced beamforming.
    • MOMV beamforming presents a promising advancement for high-performance medical ultrasound imaging.