Spatial normalization of 3D brain images using deformable models
Spatial normalization of 3D brain images using deformable models
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DOI：
10.1097/00004728-199607000-00031
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发表时间：
1996-07-01
影响因子：
1.3
通讯作者：
Davatzikos, C
中科院分区：
文献类型：
--
作者：
Davatzikos, C
目的：空间归一化和配准的断层图像从不同的主题是一个主要的问题，在几个医学成像领域，包括功能图像分析，形态计量学，和计算机辅助神经外科。这篇文章的重点是开发一种计算机化的空间归一化的3D images.Method的方法：我们提出了一种技术，是基于几何变形模型。特别是，我们首先描述了一个可变形的表面算法，找到一个数学表示的外皮质表面。基于这种表示，建立了用于获得两个不同图像中的外皮层的对应区域之间的映射的过程。这张地图随后被用来推导出一个三维弹性翘曲变换，它带来了两个图像register.Results：我们的算法的性能证明在几个数据集上。特别是，我们首先测试我们的变形表面算法的MR图像。然后，我们将MR图像配准到图谱图像。在我们的第三个实验中，我们应用程序匹配不同的皮质功能，通过曲率地图的外皮层。最后，我们应用我们的技术，从老年人大量的心室扩大的图像，我们表现出良好的注册在脑室区和周围的大脑structures.Conclusion：我们提出了一个高度自动化的方法，空间归一化的图像，使用可变形模型。我们的方法的应用包括立体定向正常化的功能和结构的图像，大脑的形态分析，和计算机辅助神经外科。
Purpose: The spatial normalization and registration of tomographic images from different subjects is a major problem in several medical imaging areas, including functional image analysis, morphometrics, and computer-aided neurosurgery. The focus of this article is the development of a computerized methodology for the spatial normalization of 3D images.Method: We propose a technique that is based on geometric deformable models. In particular, we first describe a deformable surface algorithm that finds a mathematical representation of the outer cortical surface. Based on this representation, a procedure for obtaining a map between corresponding regions of the outer cortex in two different images is established. This map is subsequently used to derive a 3D elastic warping transformation, which brings two images into register.Results: The performance of our algorithm is demonstrated on several datasets. In particular, we first test our deformable surface algorithm on MR images. We then register MR images to atlas images. In our third experiment, we apply a procedure for matching distinct cortical features identified through the curvature map of the outer cortex. Finally, we apply our technique to images from elderly individuals with substantial ventricular enlargement, and we show a good registration in the ventricular area and the surrounding brain structures.Conclusion: We present a highly automated methodology for spatial normalization of images, using deformable models. Applications of our methodology include stereotactic normalization of functional and structural images, morphological analysis of the brain, and computer-aided neurosurgery.