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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Acoustics

    Background:

    • Photoacoustic imaging is crucial for interventional procedures, requiring clear visualization of small targets.
    • Reflection artifacts from echogenic structures can obscure important signals in photoacoustic imaging.
    • Current methods struggle to differentiate true signals from noise artifacts effectively.

    Purpose of the Study:

    • To develop and validate a deep learning approach for identifying and removing reflection artifacts in photoacoustic imaging.
    • To improve the accuracy and reliability of interventional photoacoustic imaging.
    • To enable artifact-free visualization of point-like targets.

    Main Methods:

    • A convolutional neural network (CNN) was trained using simulated pre-beamformed photoacoustic data.
    • Simulations included various sound speeds, target locations, and noise levels to mimic real-world conditions.
    • The trained CNN was tested on experimental data from waterbath and phantom setups.

    Main Results:

    • The CNN achieved 100% accuracy in classifying sources and artifacts in simulated data.
    • High success rates (≥98%) were maintained for channel signal-to-noise ratios (SNRs) down to -9dB.
    • Experimental data showed high source classification accuracy (100% in waterbath, 96.67% in phantom) with minimal location error (0.40 ± 0.22 mm).

    Conclusions:

    • Deep learning, specifically CNNs, effectively identifies and removes reflection artifacts in photoacoustic imaging.
    • The developed method significantly enhances the visualization of critical targets in interventional settings.
    • This approach offers a novel solution for artifact-free photoacoustic imaging, surpassing traditional beamforming limitations.