An adaptive segmentation algorithm for time-of-flight MRA data
An adaptive segmentation algorithm for time-of-flight MRA data
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DOI：
10.1109/42.811277
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发表时间：
1999-10-01
影响因子：
10.6
通讯作者：
Noble, JA
中科院分区：
文献类型：
--
作者：
Wilson, DL;Noble, JA
脑血管形态的三维表示对于神经放射科医师治疗脑动脉瘤是必不可少的，然而，现有的成像技术不能提供这样的表示，磁共振血管成像(MRA)数据的切片只能给出二维(2D)描述，并且在X射线投影图像中产生的动脉瘤位置和大小的模糊往往是难以克服的，为了克服这些问题，我们建立了一种新的基于统计的自动算法来从飞行时间(TOF)MRA数据中提取三维血管信息。我们引入了受血流物理模型激励的数据分布，该分布被用于改进的期望最大化(EM)算法，然后使用估计的模型参数来统计地将体素分类为血管或其他脑组织类别。该算法是自适应的，因为模型拟合是递归执行的，从而对数据的局部子体进行分类。我们给出了将我们的算法应用于包含不同大小的动脉和动脉瘤结构的几个真实数据集的结果。
A three-dimensional (3-D) representation of cerebral vessel morphology is essential for neuroradiologists treating cerebral aneurysms, However, current imaging techniques cannot provide such a representation, Slices of MR angiography (MRA) data can only give two-dimensional (2-D) descriptions and ambiguities of aneurysm position and size arising in X-ray projection images can often be intractable, To overcome these problems, we have established a new automatic statistically based algorithm for extracting the 3-D vessel information from time-of-flight (TOF) MRA data. We introduce distributions for the data, motivated by a physical model of blood flow, that are used in a modified version of the expectation maximization (EM) algorithm, The estimated model parameters are then used to classify statistically the voxels into vessel or other brain tissue classes, The algorithm is adaptive because the model fitting is performed recursively so that classifications are made on local subvolumes of data, We present results from applying our algorithm to several real data sets that contain both artery and aneurysm structures of various sizes.