Abstract
The row-column method received a lot of attention for 3-D ultrasound imaging. This simplification technique reduces the number of connections required to address a 2-D array and therefore reduces the amount of data to handle. However, Row-column ultrasound imaging still has its limitations: the issues of data sparsity, speckle noise, and the spatially varying point spread function with edge artifacts must all be taken into account when building a reconstruction framework. In this work, we introduce a compensated row-column ultrasound imaging system, termed 3D-CRC, that leverages 3-D information within an extended 3-D random field model to compensate for the intrinsic limitations of row-column method. Tests on 3D-CRC and previously published row-column ultrasound imaging systems show the potential of our proposed system as an effective tool for enhancing 3-D row-column imaging.
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This research was funded by the Natural Sciences and Engineering Research Council of Canada, the Canada Research Chairs Program, and the Ontario Ministry of Research and Innovation.
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Daya, I.B., Chen, A.I.H., Shafiee, M.J., Wong, A., Yeow, J.T.W. (2017). Compensated Row-Column Ultrasound Imaging System Using Three Dimensional Random Fields. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_13
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DOI: https://doi.org/10.1007/978-3-319-59876-5_13
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