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Two-dimensional iterative projection method for subsample speckle tracking of ultrasound images
Medical & Biological Engineering & Computing ( IF 2.6 ) Pub Date : 2020-09-30 , DOI: 10.1007/s11517-020-02264-z
Brandon Rebholz 1 , Fei Zheng 1 , Mohamed Almekkawy 1
Affiliation  

Speckle tracking provides robust motion estimation necessary to create accurate post-processed images. These methods are known to be less accurate in the lateral dimension compared with the axial dimension due to the limitations on the lateral resolution of ultrasound scanning. This paper proposes a two-dimensional iterative projection (TDIP) algorithm using the Riesz transform to generate the analytic signals. The TDIP is an improvement to an already accurate speckle tracking algorithm called the phase coupled (PC) method. The PC method projects the intersection of gradients on the correlation map to the zero phase contour to estimate displacement. The TDIP method performs iterative projections and uses the aggregate of these projected locations to estimate the motion, in addition to rejecting inaccurate projections by checking them against the aggregate projection location. The TDIP additionally adopts the Riesz transform to generate two-dimensional analytic signals to improve lateral accuracy. The Riesz transform is a multidimensional extension of the Hilbert transform into the nD Euclidean space and therefore can be used to include data in both axial and lateral dimensions as opposed to the commonly used Hilbert transform which is one dimensional. The accuracy of the TDIP is quantitatively proven on simulated datasets from the Field II simulation program and on experimental data from two flow phantoms. At all cases, the TDIP is more accurate than the PC algorithm at two-dimensional displacement estimation.

The lateral estimates from the TDIP method. This method is not tracking motion within the blockage in the center of the flow channel. The channel and the blockage are both bounded by dashed, red lines.



中文翻译:

超声图像子样本散斑跟踪的二维迭代投影方法

散斑跟踪提供了创建准确的后处理图像所必需的稳健运动估计。由于超声扫描横向分辨率的限制,与轴向尺寸相比,这些方法的横向尺寸精度较低。本文提出了一种使用 Riesz 变换生成解析信号的二维迭代投影 (TDIP) 算法。TDIP 是对已经精确的散斑跟踪算法的改进,称为相位耦合 (PC) 方法。PC 方法将相关图上的梯度交点投影到零相位轮廓以估计位移。TDIP 方法执行迭代投影并使用这些投影位置的聚合来估计运动,除了通过检查聚合投影位置来拒绝不准确的投影。TDIP 还采用 Riesz 变换生成二维解析信号以提高横向精度。Riesz 变换是希尔伯特变换到 nD 欧几里得空间的多维扩展,因此可用于包含轴向和横向维度的数据,而不是常用的一维希尔伯特变换。TDIP 的准确性在来自 Field II 模拟程序的模拟数据集和来自两个流动体模的实验数据上得到了定量证明。在所有情况下,TDIP 在二维位移估计方面都比 PC 算法更准确。TDIP 还采用 Riesz 变换生成二维解析信号以提高横向精度。Riesz 变换是希尔伯特变换到 nD 欧几里得空间的多维扩展,因此可用于包含轴向和横向维度的数据,而不是常用的一维希尔伯特变换。TDIP 的准确性在来自 Field II 模拟程序的模拟数据集和来自两个流动体模的实验数据上得到了定量证明。在所有情况下,TDIP 在二维位移估计方面都比 PC 算法更准确。TDIP 还采用 Riesz 变换生成二维解析信号以提高横向精度。Riesz 变换是希尔伯特变换到 nD 欧几里得空间的多维扩展,因此可用于包含轴向和横向维度的数据,而不是常用的一维希尔伯特变换。TDIP 的准确性在来自 Field II 模拟程序的模拟数据集和来自两个流动体模的实验数据上得到了定量证明。在所有情况下,TDIP 在二维位移估计方面都比 PC 算法更准确。Riesz 变换是希尔伯特变换到 nD 欧几里得空间的多维扩展,因此可用于包含轴向和横向维度的数据,而不是常用的一维希尔伯特变换。TDIP 的准确性在来自 Field II 模拟程序的模拟数据集和来自两个流动体模的实验数据上得到了定量证明。在所有情况下,TDIP 在二维位移估计方面都比 PC 算法更准确。Riesz 变换是希尔伯特变换到 nD 欧几里得空间的多维扩展,因此可用于包含轴向和横向维度的数据,而不是常用的一维希尔伯特变换。TDIP 的准确性在来自 Field II 模拟程序的模拟数据集和来自两个流动体模的实验数据上得到了定量证明。在所有情况下,TDIP 在二维位移估计方面都比 PC 算法更准确。

TDIP 方法的横向估计。该方法不跟踪流道中心阻塞内的运动。通道和阻塞均由红色虚线界定。

更新日期:2020-11-21
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