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射频通道数据的超声成像向量量化。

Vector quantization of RF channel data for ultrasound imaging.

机构信息

Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei City, Taiwan.

出版信息

Ultrason Imaging. 2013 Jan;35(1):3-16. doi: 10.1177/0161734612471665.

Abstract

Quantization is a necessary step in a digital beamformer that should be implemented efficiently when designing an ultrasound imaging system. Several factors should be considered, including the accuracy of both amplitude and phase in the digital representation, the quantization efficiency, and the complexity of the quantization scheme. While uniform scalar quantization (SQ) is currently used in most ultrasound imaging systems, this study used simulations and experimental data to explore vector quantization (VQ) based on the signals received by adjacent elements of the transducer array generally being strongly correlated. The signal-to-quantization-noise ratio, focusing quality, and accuracy in phase aberration correction and blood velocity estimation were assessed. The results show that using VQ instead of conventional SQ reduces the quantization noise by 10 dB with minimal degradation in the focusing quality. Similarly, the performance of correlation-based phase aberration correction was not affected at this level of quantization. Nevertheless, velocity estimation accuracy was found to be more susceptible to VQ noise when the reduction in the number of bits is limited to a factor of two. One benefit of using a more efficient quantizer is the reduced data size, which directly affects the data transfer rate and is an important design consideration for a digital imaging system.

摘要

量化是数字波束形成器中的必要步骤,在设计超声成像系统时应高效地实现。在设计数字表示的幅度和相位的精度、量化效率和量化方案的复杂性等方面应考虑几个因素。虽然目前大多数超声成像系统都使用均匀标量量化(SQ),但本研究使用仿真和实验数据探索了基于换能器阵元相邻单元接收到的信号通常具有强相关性的矢量量化(VQ)。评估了信号与量化噪声比、聚焦质量以及相位误差校正和血流速度估计的准确性。结果表明,与传统的 SQ 相比,使用 VQ 可将量化噪声降低 10dB,而聚焦质量的下降最小。同样,在这种量化水平下,基于相关的相位误差校正的性能不受影响。然而,当减少的位数限制为两倍时,发现速度估计的准确性更容易受到 VQ 噪声的影响。使用更有效的量化器的一个好处是数据量减少,这直接影响数据传输速率,是数字成像系统的重要设计考虑因素。

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