一种用于超声成像的低复杂度波束转换器,基于子波束转换器和具有交叉关联的多个Apodization
Yue Shen1, Ping Wang1, Lin Tong1
1State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing, China.
Ultrasound in medicine & biology
|July 23, 2024
概括
本研究介绍了SUB-MAX超声波算法,增强图像分辨率和对比度. 它提供与复杂方法相比较的高性能,但对于实时应用程序来说,计算成本较低.
科学领域:
- 超声波成像 超声波成像
- 医疗信号处理 医疗信号处理
- 图像重建算法 图像重建算法
背景情况:
- 像DAS这样的传统超声波成像方法在分辨率和对比度上有局限性.
- 先进的技术,如最小变量 (MV) 束形提供优越的性能,但是计算密集型.
- 需要超声波算法来平衡高图像质量与实时应用的计算效率.
研究的目的:
- 提出一个新的超声波成像算法,SUB-MAX (次光束变换器和多个apodization与交叉相关联).
- 为了实现高图像分辨率和与先进方法相比的增强对比度.
- 为了保持实时超声波系统的低计算复杂性.
主要方法:
- SUB-MAX算法使用了从两个 (N/2) 元素 DAS 波束造型器中减去输出,这些波束造型器具有不对称的相中心.
- 它结合了与减去的输出相反的趋势的系数.
- 这些系数与从多对接收的apodization中得出的正常化交叉相关性 (NCC) 权重矩阵融合在一起.
主要成果:
- 与DAS,DMAS,CF和MAX相比,SUB-MAX可以显著降低点目标的半最大全宽度 (FWHM),高达52.7%.
- 与DAS,MV,DMAS和CF相比,它可以大幅增加囊的对比率 (CR),高达86.8%.
- 在老鼠瘤数据集上的实验结果显示出卓越的综合成像性能.
结论:
- SUB-MAX算法实现了高分辨率和对比度的低复杂性.
- 它的性能可与先进的梁成型技术相提并论.
- 该算法的不依赖于共变矩阵反转使其适用于实时超声波成像系统.
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