Estimation of the viscoelastic properties of vessel walls using a computational model and Doppler ultrasound
Estimation of the viscoelastic properties of vessel walls using a computational model and Doppler ultrasound
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DOI：
10.1088/0031-9155/55/12/019
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发表时间：
2010-06-21
影响因子：
3.5
通讯作者：
Cachard, Christian
中科院分区：
文献类型：
--
作者：
Balocco, Simone;Basset, Olivier;Cachard, Christian
受动脉粥样硬化影响的人的动脉以壁粘弹性改变为特征。在活体内无创评估动脉粘弹性的可能性将对早期诊断和预防该病有重要意义。本文提出了一种非迭代方法来估计血管壁Zener模型的粘弹性参数。这种方法需要同时测量流动变化和壁面位移，这可以由合适的超声多普勒仪器提供。粘弹性参数的估计是通过ARMA参数方法将理论本构方程与实验测量值进行拟合来实现的。利用动脉脉动数值模拟产生的参考数据对该方法的精度和灵敏度进行了测试，其中生理条件和模型的粘弹性参数可以适当变化。估计值与参考值定量吻合，表明受生理条件变化影响的唯一参数是粘度，即使在模拟低信噪比的情况下，粘度的相对误差也在27%左右。最后，通过对圆柱形容器模型在不同流型下的三次测量，验证了该方法的可行性，得到的参数平均估计误差为25%。
Human arteries affected by atherosclerosis are characterized by altered wall viscoelastic properties. The possibility of noninvasively assessing arterial viscoelasticity in vivo would significantly contribute to the early diagnosis and prevention of this disease. This paper presents a noniterative technique to estimate the viscoelastic parameters of a vascular wall Zener model. The approach requires the simultaneous measurement of flow variations and wall displacements, which can be provided by suitable ultrasound Doppler instruments. Viscoelastic parameters are estimated by fitting the theoretical constitutive equations to the experimental measurements using an ARMA parameter approach. The accuracy and sensitivity of the proposed method are tested using reference data generated by numerical simulations of arterial pulsation in which the physiological conditions and the viscoelastic parameters of the model can be suitably varied. The estimated values quantitatively agree with the reference values, showing that the only parameter affected by changing the physiological conditions is viscosity, whose relative error was about 27% even when a poor signal-to-noise ratio is simulated. Finally, the feasibility of the method is illustrated through three measurements made at different flow regimes on a cylindrical vessel phantom, yielding a parameter mean estimation error of 25%.