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使用有限元法的模型更新图像引导神经外科手术:大脑镰的纳入

Model-Updated Image-Guided Neurosurgery Using the Finite Element Method: Incorporation of the Falx Cerebri.

作者信息

Miga Michael I, Paulsen Keith D, Kennedy Francis E, Hartov Alex, Roberts David W

机构信息

Dartmouth College, Thayer School of Engineering, HB8000, Hanover, NH 03755, http://www.thayer.dartmouth.edu/thayer/

Dartmouth Hitchcock Medical Center, Lebanon, NH 03756.

出版信息

Med Image Comput Comput Assist Interv. 1999 Sep;1679:900-910. doi: 10.1007/10704282_98.

Abstract

Surgeons using neuronavigation have realized the value of image guidance for feature recognition as well as for the precise application of surgical instruments. Recently, there has been a growing concern about the extent of intraoperative misregistration due to tissue deformation. Intraoperative imaging is currently under evaluation but limitations related to cost effectiveness and image clarity have made its wide spread adoption uncertain. As a result, computational model-guided techniques have generated considerable appeal as an alternative approach. In this paper, we report our initial experience with enhancing our brain deformation model by explicitly adding the falx cerebri. The simulations reported show significant differences in subsurface deformation with the falx serving to damp the communication of displacement between hemispheres by as much as 4 Additionally, these calculations, based on a human clinical case, demonstrate that while cortical shift predictions correlate well with various forms of the model (70-80% of surface motion recaptured), substantial differences in subsurface deformation occurs suggesting that subsurface validation of model-guided techniques will be important for advancing this concept.

摘要

使用神经导航的外科医生已经认识到图像引导对于特征识别以及手术器械精确应用的价值。最近,人们越来越关注由于组织变形导致的术中配准误差程度。目前正在评估术中成像,但与成本效益和图像清晰度相关的局限性使得其广泛应用尚不确定。因此,计算模型引导技术作为一种替代方法产生了相当大的吸引力。在本文中,我们报告了通过明确添加大脑镰来增强我们的脑变形模型的初步经验。所报告的模拟显示,大脑镰使半球间位移的传递减弱多达4,从而在地下变形方面存在显著差异。此外,这些基于人类临床病例的计算表明,虽然皮质移位预测与各种模型形式相关性良好(重新捕获了70 - 80%的表面运动),但地下变形存在实质性差异,这表明模型引导技术的地下验证对于推进这一概念将很重要。

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