Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University, Schinkelstr. 2, 52062, Aachen, Germany,
Int J Comput Assist Radiol Surg. 2014 May;9(3):387-400. doi: 10.1007/s11548-014-0979-y. Epub 2014 Jan 30.
Brain shift, the change in configuration of the brain after opening the dura mater, is a significant problem for neuronavigation. Brain structures at intra-operative deformed positions must be matched with corresponding structures in the pre-operative 3D planning data. A method to co-register the cortical surface from intra-operative microscope images with pre-operative MRI-segmented data was developed and tested.
Automated classification of sulci on MRI-extracted cortical surfaces was tested by comparison with user guided marking of prominent sulci on an intra-operative photography. A variational registration method with a fidelity energy for 3D deformations of the cortical surface in conjunction with a higher-order, linear elastic prior energy was used for the actual registration. The minimization of this energy was performed with a regularized gradient descent scheme using finite elements for spatial discretization. The sulcal classification method was tested on eight different clinical MRI data sets by comparison of the deformed MRI scans with intra-operative photographs of the brain surface.
User intervention was required for marking sulci on the photographs demonstrating the potential for incorporating an automatic classifier. The actual registration was validated first on an artificial testbed. The complete algorithm for the co-registration of actual clinical MRI data was successful for eight different patients.
Pre-operative MRI scans can be registered to intra-operative brain surface photographs using a surface-to-surface registration method. This co-registration method has potential applications in neurosurgery, particularly during functional procedures.
硬脑膜切开后脑组织结构的改变即脑移位，是神经导航中的一个重大难题。术中变形脑区的结构必须与术前三维规划数据中的相应结构相匹配。本研究旨在开发并验证一种将显微镜术中图像的皮质表面与术前 MRI 分割数据进行配准的方法。
通过与术中摄影引导下显著脑沟的标记进行比较，测试了 MRI 提取皮质表面上脑沟的自动分类方法。采用基于变分的方法，对皮质表面的三维变形采用保真度能量，结合高阶线性弹性先验能量，用于实际配准。使用基于有限元的正则化梯度下降方案最小化该能量。通过将变形后的 MRI 扫描与脑表面的术中照片进行比较，在 8 个不同的临床 MRI 数据集上测试了脑沟分类方法。
术中照片上的脑沟标记需要用户干预，这表明可以纳入自动分类器。实际配准首先在人工测试平台上进行了验证。对于 8 名不同的患者，完整的实际临床 MRI 数据配准算法是成功的。
可以使用基于表面的配准方法将术前 MRI 扫描与术中脑表面照片进行配准。这种配准方法在神经外科，特别是在功能手术中具有潜在的应用价值。
