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An adaptive strain estimator for elastography

机译:弹性成像的自适应应变估计器

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摘要

Elastography is based on the estimation of strain due to applied tissue compression. In conventional elastography, strain is computed from the gradient of the displacement estimates between gated pre- and postcompression echo signals. Gradient-based estimation methods are known to be susceptible to noise. In elastography, in addition to the electronic noise, a principal source of estimation error is the decorrelation of the echo signal as a result of tissue compression (decorrelation noise). Temporal stretching of postcompression signals previously was shown to reduce the decorrelation noise. In this paper, we introduce a novel estimator that uses the stretch factor itself as an estimator of the strain. It uses an iterative algorithm that adaptively maximises the correlation between the pre- and postcompression echo signals by appropriately stretching the latter. We investigate the performance of this adaptive strain estimator using simulated and experimental data. The estimator has exhibited a vastly superior performance compared with the conventional gradient-based estimator.
机译:弹性成像基于对施加的组织压缩引起的应变的估计。在常规弹性成像中,根据门控压缩前和压缩后回声信号之间的位移估计值的梯度来计算应变。已知基于梯度的估计方法容易受到噪声的影响。在弹性成像中,除了电子噪声之外,估计误差的主要来源是由于组织压缩而导致的回声信号的去相关(去相关噪声)。先前显示的后压缩信号的时间拉伸可以减少去相关噪声。在本文中,我们介绍了一种新颖的估计器,它使用拉伸因子本身作为应变的估计器。它使用一种迭代算法,通过适当地拉伸后压缩信号和后压缩信号,自适应地最大化前压缩信号和后压缩信号之间的相关性。我们使用模拟和实验数据来研究这种自适应应变估计器的性能。与常规的基于梯度的估计器相比,该估计器表现出极大的优越性能。

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