Suppr超能文献

立体视觉引导的脑移位补偿。

Stereopsis-guided brain shift compensation.

作者信息

Sun Hai, Lunn Karen E, Farid Hany, Wu Ziji, Roberts David W, Hartov Alex, Paulsen Keith D

机构信息

Dartmouth Medical School, 172 Kellogg Building, Hanover, NH 03755 USA.

出版信息

IEEE Trans Med Imaging. 2005 Aug;24(8):1039-52. doi: 10.1109/TMI.2005.852075.

Abstract

Brain deformation models have proven to be a powerful tool in compensating for soft tissue deformation during image-guided neurosurgery. The accuracy of these models can be improved by incorporating intraoperative measurements of brain motion. We have designed and implemented a passive intraoperative stereo vision system capable of estimating the three-dimensional shape of the surgical scene in near real-time. This intraoperative shape is compared with the cortical surface in the co-registered preoperative magnetic resonance (MR) volume for the estimation of the cortical motion resulting from the open cranial surgery. The estimated cortical motion is then used to guide a full brain model, which updates a preoperative MR volume. We have found that the stereo vision system is accurate to within approximately 1 mm. Based on data from two representative clinical cases, we show that stereopsis guidance improves the accuracy of brain shift compensation both at and below the cortical surface.

摘要

脑形变模型已被证明是在图像引导神经外科手术中补偿软组织形变的有力工具。通过纳入术中脑运动测量可以提高这些模型的准确性。我们设计并实现了一种被动式术中立体视觉系统,能够近乎实时地估计手术场景的三维形状。将此术中形状与配准后的术前磁共振(MR)容积中的皮质表面进行比较,以估计开颅手术引起的皮质运动。然后,将估计出的皮质运动用于引导全脑模型,该模型会更新术前MR容积。我们发现立体视觉系统的精度在约1毫米以内。基于两个具有代表性的临床病例数据,我们表明立体视觉引导提高了皮质表面及以下脑移位补偿的准确性。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验