Reconstruction of elasticity: a stochastic model-based approach in ultrasound elastography.
Reconstruction of elasticity: a stochastic model-based approach in ultrasound elastography.
复制标题
弹性重建：超声弹性成像中基于随机模型的方法
DOI：
10.1186/1475-925x-12-79
复制
发表时间：
2013-08-10
影响因子：
3.9
通讯作者：
Liu H
中科院分区：
文献类型：
--
作者：
Lu M;Zhang H;Wang J;Yuan J;Hu Z;Liu H
传统的基于应变的算法在临床上得到了广泛的应用。它只能提供组织刚度的相对信息。然而，组织刚度的准确信息应该是有价值的临床诊断和treatment.MethodsIn这项研究中，我们提出了一种重建策略，以恢复组织的机械性能。在将生物力学模型和数据之间的差异建模为过程噪声，并将生物力学模型约束转换为状态空间表示之后，可以通过一个滤波识别过程来实现弹性的重建，该过程是根据最小均方误差（MMSE）准则从超声数据递归地估计材料属性和运动学函数。在实现这种基于模型的算法时，采用线性各向同性弹性作为生物力学约束。运动学函数的估计（即，的完整的位移和速度场），和杨氏模量的分布计算同时通过扩展卡尔曼滤波器（EKF）。ResultsIn以下实验中的准确性和鲁棒性的这个过滤框架的第一次评估在控制条件下的合成数据，然后评估这个框架的性能在真实的数据收集的弹性体模和患者使用的超声系统。定量分析表明，该滤波策略估计的应变场更接近地面实况。杨氏模量的分布也得到了很好的估计。此外，测量噪声和过程噪声的影响已经调查，以及.ConclusionsThe优势，这种基于模型的算法比传统的基于应变的算法是它的潜力，提供一个适当的生物力学模型约束下的弹性分布。我们通过在我们的框架中引入过程噪声和测量噪声来解决模型数据的差异和测量噪声，然后通过MMSE意义下的EFK来估计杨氏模量的绝对值。然而，初始条件和网格策略会影响性能，即，收敛速度和计算代价等。
BackgroundThe convectional strain-based algorithm has been widely utilized in clinical practice. It can only provide the information of relative information of tissue stiffness. However, the exact information of tissue stiffness should be valuable for clinical diagnosis and treatment.MethodsIn this study we propose a reconstruction strategy to recover the mechanical properties of the tissue. After the discrepancies between the biomechanical model and data are modeled as the process noise, and the biomechanical model constraint is transformed into a state space representation the reconstruction of elasticity can be accomplished through one filtering identification process, which is to recursively estimate the material properties and kinematic functions from ultrasound data according to the minimum mean square error (MMSE) criteria. In the implementation of this model-based algorithm, the linear isotropic elasticity is adopted as the biomechanical constraint. The estimation of kinematic functions (i.e., the full displacement and velocity field), and the distribution of Young’s modulus are computed simultaneously through an extended Kalman filter (EKF).ResultsIn the following experiments the accuracy and robustness of this filtering framework is first evaluated on synthetic data in controlled conditions, and the performance of this framework is then evaluated in the real data collected from elastography phantom and patients using the ultrasound system. Quantitative analysis verifies that strain fields estimated by our filtering strategy are more closer to the ground truth. The distribution of Young’s modulus is also well estimated. Further, the effects of measurement noise and process noise have been investigated as well.ConclusionsThe advantage of this model-based algorithm over the conventional strain-based algorithm is its potential of providing the distribution of elasticity under a proper biomechanical model constraint. We address the model-data discrepancy and measurement noise by introducing process noise and measurement noise in our framework, and then the absolute values of Young’s modulus are estimated through the EFK in the MMSE sense. However, the initial conditions, and the mesh strategy will affect the performance, i.e., the convergence rate, and computational cost, etc.