Biomechanical modeling of the human head for physically based, nonrigid image registration
Biomechanical modeling of the human head for physically based, nonrigid image registration
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DOI：
10.1109/42.811267
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发表时间：
1999-10-01
影响因子：
10.6
通讯作者：
Gilsbach, JM
中科院分区：
文献类型：
--
作者：
Hagemann, A;Rohr, K;Gilsbach, JM
影像引导神经外科手术的准确性通常会因术中变化而导致脑变形。这些变形导致解剖几何学(器官形态和器官间空间关系)的显著变化，从而使术中基于术前图像的导航容易出错。为了提高导航精度，我们建立了一个基于有限元方法的人头生物力学模型，用于对手术前图像进行校正，以处理手术过程中发生的变形。在目前的发展阶段，该模型的二维(2-D)实现由两种不同的材料组成，尽管该理论适用于三维(3-D)情况并能够处理任意数量的不同材料。对于术前图像的校正，必须指定一组确定对应关系的同源标志。这些对应关系可以很容易地集成到模型中，并在术前图像变形的整个计算过程中保持不变。通过全面的文献研究，确定了必要的材料参数值。我们的方法已经在合成图像的情况下进行了测试，并产生了物理上可信的变形结果。此外，我们对术前人头的MR图像和相应的术后图像进行了配准实验，模拟了术中图像。我们发现，我们的方法得到了很好的预测结果，即使在只在相对较小的图像区域给出对应的情况下也是如此。
The accuracy of image-guided neurosurgery generally suffers from brain deformations due to intraoperative changes. These deformations cause significant changes of the anatomical geometry (organ shape and spatial interorgan relations), thus making intraoperative navigation based on preoperative images error prone. In order to improve the navigation accuracy, we developed a biomechanical model of the human head based on the finite element method, which can be employed for the correction of preoperative images to cope with the deformations occurring during surgical interventions. At the current stage of development, the two-dimensional (2-D) implementation of the model comprises two different materials, though the theory holds for the three-dimensional (3-D) case and is capable of dealing with an arbitrary number of different materials. For the correction of a preoperative image, a set of homologous landmarks must be specified which determine correspondences. These correspondences can be easily integrated into the model and are maintained throughout the computation of the deformation of the preoperative image. The necessary material parameter values have been determined through a comprehensive literature study. Our approach has been tested for the case of synthetic images and yields physically plausible deformation results. Additionally, we carried out registration experiments with a preoperative MR image of the human head and a corresponding postoperative image simulating an intraoperative image. We found that our approach yields good prediction results, even in the case when correspondences are given in a relatively small area of the image only.