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A Deep Learning Method for Motion Artifact Correction in Intravascular Photoacoustic Image Sequence.

Sun Zheng, Du Jiejie, Yao Yue

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    |August 29, 2022
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    This summary is machine-generated.

    This study introduces a deep learning method to correct motion artifacts in intravascular photoacoustic (IVPA) imaging of coronary arteries. The novel approach enhances image quality without discarding data, improving diagnostic value.

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    Area of Science:

    • Biomedical Imaging
    • Artificial Intelligence in Medicine
    • Cardiovascular Imaging

    Background:

    • Intravascular photoacoustic (IVPA) imaging is crucial for coronary artery assessment.
    • Motion artifacts from cardiac cycles significantly degrade IVPA image quality.
    • Current gating methods can lead to loss of valuable diagnostic information.

    Purpose of the Study:

    • To develop a deep learning-based method for motion artifact correction in non-gated IVPA imaging.
    • To preserve diagnostically valuable information lost in traditional gating techniques.
    • To validate the method's effectiveness in in vivo intracoronary imaging.

    Main Methods:

    • A deep learning network, MAC-Net, was designed to correct motion artifacts in IVPA sequences.
    • Raw signal frames were clustered into dynamic and static categories.
    • The network was trained and tested using a computer-generated dataset.
    • The method was validated using in vivo intravascular ultrasound and optical coherence tomography data.

    Main Results:

    • The MAC-Net successfully corrected motion artifacts in dynamic frames without discarding data.
    • Quantitative evaluation showed improved visual quality and reduced inter-frame dissimilarity.
    • The method demonstrated motion suppression comparable to gating and image registration techniques.
    • Feasibility was confirmed in in vivo intracoronary imaging scenarios.

    Conclusions:

    • Deep learning offers a powerful solution for motion artifact correction in non-gated IVPA imaging.
    • The proposed method enhances image quality and preserves data integrity for better coronary artery assessment.
    • This technique holds promise for improving intracoronary imaging diagnostics.