Machine Learning Approaches for Quantitative Viscoelastic Response (QVisR) Ultrasound
Machine Learning Approaches for Quantitative Viscoelastic Response (QVisR) Ultrasound
复制标题
定量粘弹性响应 (QVisR) 超声的机器学习方法
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
C. Gallippi
中科院分区：
文献类型：
--
作者：
Joseph B. Richardson;Christopher J. Moore;Keerthi S. Anand;Keita A. Yokoyama;C. Gallippi
我们提出了粘弹性响应（VisR）超声的定量扩展，从硅上的轴上VisR位移剖面估计剪切弹性和粘性模量。各向同性，均匀，线性粘弹性材料，剪切弹性范围为1.57-33.33 kPa, 0.0033-2.34 Pa。在26个震源深度处，模拟了VisR波束序列对s剪切粘度的影响。在给定位移剖面、震源深度和轴向深度的情况下，使用多目标回归机器学习模型来估计剪切弹性和剪切粘度。表现最好的模型的剪切弹性RMSE为0.29 kPa，剪切粘度RMSE为0.13 Pa。S表示在测试集上进行预测。这些结果表明，机器学习方法可用于从VisR位移剖面定量估计粘弹性。
We present a quantitative extension of Viscoelastic Response (VisR) ultrasound that estimates shear elastic and viscous moduli from on-axis VisR displacement profiles in silico. Isotropic, homogeneous, linearly viscoelastic materials ranging from 1.57-33.33 kPa shear elasticity and 0.0033-2.34 Pa.s shear viscosity subject to a VisR beamsequence at 26 focal depths were simulated. Multi-target regression machine learning models were used to estimate shear elasticity and shear viscosity given the displacement profile, focal depth, and axial depth. The best performing models achieve a shear elasticity RMSE of 0.29 kPa and a shear viscosity RMSE of 0.13 Pa.s when predictions were made on the test set. These results suggest that machine learning methods can be used to quantitatively estimate viscoelasticity from VisR displacement profiles.
影响因子：
2.9
作者：
Hossain MM;Selzo MR;Hinson RM;Baggesen LM;Detwiler RK;Chong WK;Burke LM;Caughey MC;Fisher MW;Whitehead SB;Gallippi CM
通讯作者：
Gallippi CM