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A Spline Interpolation-based Data Reconstruction Technique for Estimation of Strain Time Constant in Ultrasound Poroelastography

机译:基于样条插值的数据重构,用于估计超声波络像中的应变时间常数

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Ultrasound poroelastography is a cost-effective and noninvasive imaging technique, which can be used to reconstruct mechanical parameters of tissues such as Young's modulus, Poisson's ratio, interstitial permeability, and vascular permeability. To estimate interstitial permeability and vascular permeability using poroelastography, accurate estimation of the strain time constant (TC) is required. This can be a challenging task due to the nonlinearity of the exponential strain curve and noise affecting the experimental data. Due to motion artifacts caused by the sonographer, animal/patient, and/or the environment, noise affecting some strain frames can be significantly higher than the strain signal. If these frames are used for the computation of the strain TC, the resulting TC estimate can be highly inaccurate, which, in turn, can cause high errors in the reconstructed mechanical parameters. In this paper, we introduce a cubic spline-based interpolation method, which allows to use only good quality strain frames (i.e., frames with sufficiently high signal-to-noise ratio [SNR]) to estimate the strain TC. Using finite element simulations, we demonstrate that the proposed interpolation method can improve the estimation accuracy of the strain TC by 46% with respect to the case where no interpolation and filtering are used and by 37% with respect to the case where the strain frames are Kalman filtered before TC estimation (at an SNR of 30 dB). We also prove the technical feasibility of the proposed technique using in vivo experimental data. While detecting the bad frames in both simulations and experiments, we assumed the lower limit SNR to be below 10 dB. Based on our results, the proposed technique may be of great help in applications relying on the accurate assessment of the temporal behavior of strain data.
机译:超声波佐料术是一种成本效益和非侵入性的成像技术,可用于重建杨氏模量,泊松比,间质渗透性和血管渗透性等组织的机械参数。为了估算使用散文术的间质渗透性和血管渗透性,需要精确估计应变时间常数(Tc)。由于指数应变曲线的非线性和影响实验数据的噪声,这可能是一个具有挑战性的任务。由于由超声师,动物/患者和/或环境引起的运动伪影,影响一些应变帧的噪声可以明显高于应变信号。如果这些帧用于计算应变Tc,则产生的Tc估计可以高度准确,这反过来又可以在重建的机械参数中引起高误差。在本文中,我们介绍了基于立方样条的插值方法,其允许仅使用良好的质量应变帧(即,具有足够高信噪比φ)的良好质量框架(即,帧)来估计应变Tc。使用有限元模拟,我们证明所提出的内插方法可以相对于没有使用插值和滤波的情况和相对于应变帧的情况的情况提高应变Tc的估计精度46% Kalman在TC估计之前过滤(在30 dB的SNR处)。我们还证明了在体内实验数据中使用所提出的技术的技术可行性。在仿真和实验中检测到糟糕帧时,我们假设下限SNR低于10 dB。根据我们的结果,所提出的技术在依赖于准确评估应变数据的时间行为的应用中可能存在很大的帮助。

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