28 April 2004 Compact implementation of dynamic receive apodization in ultrasound scanners
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Abstract
The image quality in medical ultrasound scanners is determined by several factors, one of which is the ability of the receive beamformer to change the aperture weighting function with depth and beam angle. In digital beamformers, precise dynamic apodization can be achieved by representing that function by numeric sequences. For a 15 cm scan depth and 100 lines per image, a 64-channel, 40 MHz ultrasound beamformer may need almost 50 million coefficients. A more coarse representation of the aperture relieves the memory requirements but does not enable compact and precise beamforming. Previously, the authors have developed a compact beamformer architecture which utilizes sigma-delta A/D conversion, recursive delay generation and sparse sample reconstruction using FIR filters. The channel weights were here fixed. In this paper, a compact implementation of dynamic receive apodization is presented. It allows precise weighting coefficient generation and utilizes a recursive algorithm which shares its starting parameters with the recursive delay generation logic. Thus, only a separate calculation block, consisting of 5 adders and 5 registers, is necessary. A VHDL implementation in a Xilinx XCV2000E-7 FPGA has been made for the whole receive beamformer for assessing the necessary hardware resources and the achievable performance for that platform. The code implements dynamic apodization with an expanding aperture for either linear or phased array imaging. A complete 32-channel beamformer can operate at 129.82 MHz and occupies 1.28 million gates. Simulated in Matlab, a 64-channel beamformer provides gray scale image with around 55 dB dynamic range. The beamformed data can also be used for flow estimation.
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Borislav Gueorguiev Tomov, Borislav Gueorguiev Tomov, Jørgen Arendt Jensen, Jørgen Arendt Jensen, } "Compact implementation of dynamic receive apodization in ultrasound scanners", Proc. SPIE 5373, Medical Imaging 2004: Ultrasonic Imaging and Signal Processing, (28 April 2004); doi: 10.1117/12.532957; https://doi.org/10.1117/12.532957
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