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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >On the Performance of Time Domain Displacement Estimators for Magnetomotive Ultrasound Imaging
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On the Performance of Time Domain Displacement Estimators for Magnetomotive Ultrasound Imaging

机译：磁动力超声成像的时域位移估计器性能

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摘要

In magnetomotive (MM) ultrasound (US) imaging, magnetic nanoparticles (NPs) are excited by an external magnetic field and the tracked motion of the surrounding tissue serves as a surrogate parameter for the NP concentration. MMUS procedures exhibit weak displacement contrasts due to small forces on the NPs. Consequently, precise US-based displacement estimation is crucial in terms of a sufficiently high contrast-to-noise ratio (CNR) in MMUS imaging. Conventional MMUS detection of the NPs is based on samplewise evaluation of the phase of the in-phase and quadrature (IQ) data, where a low signal-to-noise ratio (SNR) in the data leads to strong phase noise and, thus, to an increased variance of the displacement estimate. This paper examines the performance of two time-domain displacement estimators (DEs) for MMUS imaging, which differ from conventional MMUS techniques by incorporating data from an axial segment. The normalized cross correlation (NCC) estimator and a recursive Bayesian estimator, incorporating spatial information from neighboring segments, weighted by the local SNR, are adapted for the small MMUS displacement magnitudes. Numerical simulations of MM-induced, time-harmonic bulk and Gaussian-shaped displacement profiles show that the two time-domain estimators yield a reduced estimation error compared to the phase-shift-based estimator. Phantom experiments, using our recently proposed magnetic excitation setup, show a 1.9-fold and 3.4-fold increase of the CNR in the MMUS images for the NCC and Bayes estimator compared to the conventional method, while the amount of required data is reduced by a factor of 100.
机译：在磁动力（MM）超声（US）成像中，磁性纳米颗粒（NPs）被外部磁场激发，周围组织的跟踪运动成为NP浓度的替代参数。 MMUS程序由于对NP的作用力较小而显示出较弱的位移对比。因此，就MMUS成像中足够高的对比度噪声比（CNR）而言，基于美国的精确位移估计至关重要。 NP的常规MMUS检测是基于对同相和正交（IQ）数据的相位进行采样评估的，其中数据中的低信噪比（SNR）会导致强烈的相位噪声，因此，位移估计的方差增加。本文研究了两种时域位移估计器（DE）的MMUS成像性能，它们与传统的MMUS技术有所不同，其方法是合并了轴向部分的数据。归一化互相关（NCC）估计器和递归贝叶斯估计器，结合了来自相邻段的空间信息，并通过局部SNR加权，适用于较小的MMUS位移幅度。 MM引起的时谐整体和高斯形位移剖面的数值模拟表明，与基于相移的估计器相比，这两个时域估计器的估计误差减小了。使用我们最近提出的磁激励装置进行的幻影实验显示，与传统方法相比，NCC和贝叶斯估计器的MMUS图像中CNR分别增加了1.9倍和3.4倍，而所需数据量却减少了系数为100。

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