An improved minimum variance beamforming applied to plane-wave imaging in medical ultrasound

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    Abstract

    Minimum variance beamformer (MVB) is an adaptive beamformer which provides images with higher
    resolution and contrast in comparison with non-adaptive beamformers like delay and sum (DAS). It finds weight vector of beamformer by minimizing output power while keeping the desired signal unchanged. We used the eigen-based MVB and generalized coherence factor (GCF) to further improve the quality of MVB beamformed images. The eigen-based MVB projects the weight vector with a transformation matrix
    constructed from eigen-decomposing of the array covariance matrix that increases resolution and contrast. GCF is used to emphasis on coherence part of images that improves the resolution. Four different datasets provided by IUS 2016 beamforming challenge are used to evaluate the proposed
    method. In comparison with DAS with rectangular weight vector, our method improved contrast about 8.52 dB and 6.20 dB for simulation and experimental contrast phantoms, respectively. It also enhanced lateral (axial) resolution about 87% (40%) and 73% (21%) for simulated and experimental resolution phantoms, respectively.
    Original languageEnglish
    Title of host publicationProceedings of 2016 IEEE International Ultrasonics Symposium
    Number of pages4
    PublisherIEEE
    Publication date2016
    ISBN (Print)978-1-4673-9897-8
    DOIs
    Publication statusPublished - 2016
    Event2016 IEEE International Ultrasonics Symposium - Convention Center Vinci Tours, Tours, France
    Duration: 18 Sept 201621 Sept 2016
    https://ieeexplore.ieee.org/xpl/conhome/7589760/proceeding

    Conference

    Conference2016 IEEE International Ultrasonics Symposium
    LocationConvention Center Vinci Tours
    Country/TerritoryFrance
    CityTours
    Period18/09/201621/09/2016
    Internet address

    Keywords

    • Medical ultrasound imaging
    • Adaptive Beamforming
    • Minimum variance beamformer
    • Plane wave imaging

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