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一种用于识别组织力学参数的非刚性图像配准框架。

A nonrigid image registration framework for identification of tissue mechanical parameters.

作者信息

Jordan Petr, Socrate Simona, Zickler Todd E, Howe Robert D

机构信息

Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA.

出版信息

Med Image Comput Comput Assist Interv. 2008;11(Pt 2):930-8. doi: 10.1007/978-3-540-85990-1_112.

Abstract

We present a modular framework for mechanically regularized nonrigid image registration of 3D ultrasound and for identification of tissue mechanical parameters. Mechanically regularized deformation fields are computed from sparsely estimated local displacements. We enforce image-based local motion estimates by applying concentrated forces at mesh nodes of a mechanical finite-element model. The concentrated forces are generated by the elongation of regularization springs connected to the mesh nodes as their free ends are displaced according to local motion estimates. The regularization energy corresponding to the potential energy stored in the springs is minimized when the mechanical response of the model matches the observed response of the organ. We demonstrate that this technique is suitable for identification of material parameters of a nonlinear viscoelastic liver model and demonstrate its benefits over traditional indentation methods in terms of improved volumetric agreement between the model response and the experiment.

摘要

我们提出了一个模块化框架,用于对三维超声进行机械正则化非刚性图像配准以及识别组织力学参数。机械正则化变形场由稀疏估计的局部位移计算得出。我们通过在机械有限元模型的网格节点上施加集中力来强制基于图像的局部运动估计。当连接到网格节点的正则化弹簧的自由端根据局部运动估计发生位移时,弹簧伸长从而产生集中力。当模型的力学响应与器官的观测响应相匹配时,与弹簧中存储的势能相对应的正则化能量最小化。我们证明了该技术适用于识别非线性粘弹性肝脏模型的材料参数,并在模型响应与实验之间的体积一致性提高方面,展示了其相对于传统压痕方法的优势。

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