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Related Experiment Video

Updated: Nov 16, 2025

Three-dimensional Optical-resolution Photoacoustic Microscopy
08:31

Three-dimensional Optical-resolution Photoacoustic Microscopy

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Subaperture Processing-Based Adaptive Beamforming for Photoacoustic Imaging.

Rashid Al Mukaddim, Rifat Ahmed, Tomy Varghese

    IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
    |February 19, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Photoacoustic (PA) imaging noise is reduced using a new method called PA subaperture processing (PSAP). PSAP improves image quality and target detection in preclinical cardiac PA imaging.

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    Area of Science:

    • Biomedical optics
    • Medical imaging
    • Acoustics

    Background:

    • Delay-and-sum (DAS) beamforming in photoacoustic (PA) imaging suffers from strong sidelobes and off-axis clutter due to lack of transmit focusing.
    • These artifacts degrade image quality, particularly in preclinical cardiac PA imaging, hindering the detection of myocardial signals and cardiac pathology.
    • Current methods struggle to effectively suppress noise while preserving diagnostic information.

    Purpose of the Study:

    • To introduce and validate PA subaperture processing (PSAP), an adaptive beamforming technique for mitigating sidelobes and clutter in PA image reconstruction.
    • To enhance the detectability of PA signals from the myocardial wall for improved cardiac PA imaging.
    • To quantitatively assess the performance of PSAP against conventional DAS and DAS-CF methods.

    Main Methods:

    • PSAP involves splitting received channel data into two subapertures to form two DAS reconstructed images.
    • A weighting matrix is generated by analyzing the correlation between these subaperture images.
    • This matrix is applied to the full-aperture DAS PA image to suppress sidelobes and incoherent clutter.

    Main Results:

    • Numerical simulations (point target, diffuse inclusion, microvasculature) and in vivo murine studies demonstrated PSAP's effectiveness.
    • PSAP significantly improved image quality, target detectability, and clutter suppression compared to DAS and DAS-CF.
    • PSAP achieved higher generalized contrast-to-noise (gCNR) ratios (19.61% vs DAS, 19.53% vs DAS-CF) and contrast ratios (CR) (89.26% vs DAS, 11.90% vs DAS-CF) in vasculature imaging.

    Conclusions:

    • PSAP is an effective adaptive beamforming method for reducing sidelobes and clutter in PA imaging.
    • The technique offers significant improvements in image quality and quantitative metrics, particularly for preclinical cardiac applications.
    • PSAP shows great potential for advancing diagnostic capabilities in PA imaging by enhancing visualization of fine vascular structures and cardiac pathology.