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    This study introduces a novel switchable and tunable deep beamformer for ultrasound imaging. This single deep learning model efficiently generates diverse outputs and adjusts noise levels, reducing resource demands.

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    Area of Science:

    • Medical Imaging
    • Deep Learning
    • Ultrasound Technology

    Background:

    • Deep learning beamformers offer computational efficiency and versatility in ultrasound imaging.
    • Current deep beamformers require extensive training and storage for varied applications, demanding significant resources.

    Purpose of the Study:

    • To develop a single, adaptable deep beamformer for ultrasound imaging that can generate multiple output types and adjust noise reduction.
    • To overcome the resource limitations associated with training numerous specialized deep beamformers.

    Main Methods:

    • Implementation of a switchable and tunable deep beamformer using Adaptive Instance Normalization (AdaIN) layers.
    • Enabling the single generator to produce distinct outputs (e.g., DAS, MVBF, DMAS, GCF) and control noise levels via AdaIN codes.

    Main Results:

    • Demonstrated flexibility and efficacy of the proposed deep beamformer using B-mode focused ultrasound data.
    • Successful generation of various beamforming outputs and adjustable noise levels from a single model.

    Conclusions:

    • The proposed switchable and tunable deep beamformer significantly enhances efficiency and reduces resource requirements in ultrasound imaging.
    • This adaptable approach offers a versatile solution for diverse ultrasound applications, paving the way for more streamlined deep learning integration.