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Related Experiment Video

Updated: Oct 10, 2025

Three-dimensional Optical-resolution Photoacoustic Microscopy
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Improving Minimum Variance Beamforming with Sub-Aperture Processing for Photoacoustic Imaging.

Rashid Al Mukaddim, Rifat Ahmed, Tomy Varghese

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    A new photoacoustic imaging (PAI) method, PSAPMV, enhances image quality by reducing sidelobes and clutter. This adaptive beamforming technique improves resolution and target detectability compared to existing methods.

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    Area of Science:

    • Medical Imaging
    • Biomedical Engineering
    • Acoustics

    Background:

    • Minimum variance (MV) beamforming offers improved resolution and reduced sidelobes in photoacoustic imaging (PAI) over delay-and-sum (DAS).
    • Persistent sidelobe signals and incoherent clutter in MV-based PAI still degrade image quality.

    Purpose of the Study:

    • To introduce and validate a novel adaptive beamforming algorithm, PSAPMV, for enhanced PAI quality.
    • To suppress sidelobe signals and incoherent clutter in PAI using sub-aperture processing combined with MV beamforming.

    Main Methods:

    • The proposed PSAPMV algorithm splits received channel data into complementary sub-apertures, applies MV beamforming to each, and uses a similarity-based weighting matrix.
    • A weighting matrix derived from sub-aperture image similarity is applied to the full-aperture MV image to suppress artifacts.
    • Numerical simulations with point targets, diffuse inclusions, and microvasculature networks were used for validation.

    Main Results:

    • PSAPMV demonstrated superior qualitative and quantitative beamforming performance.
    • In simulations, PSAPMV achieved higher resolution (FWHM = 0.19 mm) than MV (0.21 mm) and DAS (0.22 mm).
    • PSAPMV improved target detectability (gCNR = 0.99) and contrast (CR = 51.74 dB) compared to MV and DAS methods.

    Conclusions:

    • The PSAPMV algorithm effectively suppresses sidelobe signals and incoherent clutter in PAI.
    • PSAPMV offers significant improvements in resolution, target detectability, and contrast for photoacoustic imaging applications.