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首页> 外文期刊>Medical image analysis >Serial registration of intraoperative MR images of the brain.
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Serial registration of intraoperative MR images of the brain.

机译：术中脑部MR图像的序列注册。

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摘要

The increased use of image-guided surgery systems during neurosurgery has brought to prominence the inaccuracies of conventional intraoperative navigation systems caused by shape changes such as those due to brain shift. We propose a method to track the deformation of the brain and update preoperative images using intraoperative MR images acquired at different crucial time points during surgery. We use a deformable surface matching algorithm to capture the deformation of boundaries of key structures (cortical surface, ventricles and tumor) throughout the neurosurgical procedure, and a linear finite element elastic model to infer a volumetric deformation. The boundary data are extracted from intraoperative MR images using a real-time intraoperative segmentation algorithm. The algorithm has been applied to a sequence of intraoperative MR images of the brain exhibiting brain shift and tumor resection. Our results characterize the brain shift after opening of the dura and at the different stages of tumor resection, and brain swelling afterwards. Analysis of the average deformation capture was assessed by comparing landmarks identified manually and the results indicate an accuracy of 0.7+/-0.6 mm (mean+/-S.D.) for boundary surface landmarks, of 0.9+/-0.6 mm for landmarks inside the boundary surfaces, and 1.6+/-0.9 mm for landmarks in the vicinity of the tumor.
机译：在神经外科手术中越来越多地使用图像引导手术系统已引起人们对由于形状变化（例如由于脑移动引起的形状变化）引起的常规术中导航系统的不准确性的关注。我们提出一种方法来跟踪大脑的变形并使用术中在不同关键时间点获取的术中MR图像更新术前图像。我们使用可变形表面匹配算法来捕获整个神经外科手术过程中关键结构（皮质表面，心室和肿瘤）边界的变形，并使用线性有限元弹性模型来推断体积变形。使用实时术中分割算法从术中MR图像中提取边界数据。该算法已应用于显示脑移位和肿瘤切除术的一系列术中大脑MR图像。我们的结果表征了硬脑膜打开后和肿瘤切除的不同阶段的脑转移，然后脑肿胀。通过比较手动识别的界标来评估平均变形捕获的分析，结果表明边界面界标的精度为0.7 +/- 0.6 mm（平均值+/- SD），边界面内界标的精度为0.9 +/- 0.6 mm ，对于肿瘤附近的界标为1.6 +/- 0.9 mm。

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