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Stochastic Piecewise Linear Function Fitting with Application to Ultrasound Shear Wave Imaging

机译:随机分段线性函数拟合在超声剪切波成像中的应用

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摘要

Piecewise linear function fitting is ubiquitous in many signal processing applications. Inspired by an application to shear wave velocity imaging in ultrasound elastography, this paper presents a discrete state-space Markov model for noisy piecewise linear data and also proposes a tractable algorithm for maximum a posteriori estimation of the slope of each segment in the piecewise linear function. The number and locations of breaks is handled indirectly by the stochastics of the Markov model. In the ultrasound shear wave imaging application, these slope values have concrete physical interpretation as being the reciprocal of the shear wave velocities in the imaged medium. Data acquired on an ellipsoidal inclusion phantom shows that this algorithm can provide good contrast of around 6 dB and contrast to noise ratio of 25 dB between the stiff inclusion and surrounding soft background. The phantom validation study also shows that this algorithm can be used to preserve sharp boundary details, which would otherwise be blurred out if a sliding window least squares filter is applied.
机译:分段线性函数拟合在许多信号处理应用中无处不在。受到超声弹性成像中剪切波速度成像应用的启发,本文提出了一种用于嘈杂分段线性数据的离散状态空间马尔可夫模型,并提出了一种可处理的算法,以使分段线性函数中每个段的斜率最大后验估计。中断的数量和位置由马尔可夫模型的随机性间接处理。在超声剪切波成像应用中,这些斜率值具有具体的物理解释,是所成像介质中剪切波速度的倒数。在椭球形夹杂物模型上获取的数据表明,该算法可以提供大约6 dB的良好对比度,并且在刚性夹杂物和周围的柔和背景之间的噪声比为25 dB。幻像验证研究还表明,该算法可用于保留清晰的边界细节,如果应用了滑动窗口最小二乘滤镜,则边界细节将被模糊掉。

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