定向张量重建:从扩散张量MRI追踪神经通路

L. Zhukov, A. Barr
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引用次数: 128

摘要

本文提出了一种利用三维张量场跟踪解剖纤维的新方法。该技术使用局部正则化技术提取显著张量特征,该技术允许算法跨越噪声区域并弥合数据中的间隙。我们将该方法应用于人脑DT-MRI数据,并恢复了与白质脑纤维通路对应的可识别的解剖结构。本文中的图像来源于分辨率为121/spl倍/88/spl倍/60的数据集。通过应用正则化技术,即使用关于纤维平滑度的先验假设,我们能够以小于体素大小的分辨率恢复纤维。正则化过程是通过直接纳入跟踪算法的移动最小二乘滤波器完成的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Oriented tensor reconstruction: tracing neural pathways from diffusion tensor MRI
In this paper we develop a new technique for tracing anatomical fibers from 3D tensor fields. The technique extracts salient tensor features using a local regularization technique that allows the algorithm to cross noisy regions and bridge gaps in the data. We applied the method to human brain DT-MRI data and recovered identifiable anatomical structures that correspond to the white matter brain-fiber pathways. The images in this paper are derived from a dataset having 121/spl times/88/spl times/60 resolution. We were able to recover fibers with less than the voxel size resolution by applying the regularization technique, i.e., using a priori assumptions about fiber smoothness. The regularization procedure is done through a moving least squares filter directly incorporated in the tracing algorithm.
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