使用扩散张量成像纤维束造影术进行整合性脑区划分与标准化
Integrated parcellation and normalization using DTI fasciculography.
作者信息
Ho Hon Pong, Wang Fei, Papademetris Xenophon, Blumberg Hilary P, Staib Lawrence H
机构信息
Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
出版信息
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):33-41. doi: 10.1007/978-3-642-23629-7_5.
Existing methods for fiber tracking, interactive bundling and editing from Diffusion Magnetic Resonance Images (DMRI) reconstruct white matter fascicles using groups of virtual pathways. Classical numerical fibers suffer from image noise and cumulative tracking errors. 3D visualization of bundles of fibers reveals structural connectivity of the brain; however, extensive human intervention, tracking variations and errors in fiber sampling make quantitative fascicle comparison difficult. To simplify the process and offer standardized white matter samples for analysis, we propose a new integrated fascicle parcellation and normalization method that combines a generic parametrized volumetric tract model with orientation information from diffusion images. The new technique offers a tract-derived spatial parameter for each voxel within the model. Cross-subject statistics of tract data can be compared easily based on these parameters. Our implementation demonstrated interactive speed and is available to the public in a packaged application.
现有用于从扩散磁共振图像（DMRI）进行纤维追踪、交互式捆绑和编辑的方法使用虚拟路径组重建白质束。传统的数值纤维受图像噪声和累积追踪误差的影响。纤维束的三维可视化揭示了大脑的结构连通性；然而，大量的人工干预、追踪变化以及纤维采样中的误差使得束的定量比较变得困难。为了简化流程并提供标准化的白质样本用于分析，我们提出了一种新的综合束分割和归一化方法，该方法将通用的参数化体积束模型与来自扩散图像的方向信息相结合。新技术为模型内的每个体素提供了一个源自束的空间参数。基于这些参数，可以轻松比较束数据的跨个体统计信息。我们的实现展示了交互式速度，并且以打包应用程序的形式向公众开放。
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