Degeneracy-aware interpolation of 3D diffusion tensor fields
Degeneracy-aware interpolation of 3D diffusion tensor fields
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DOI：
10.1117/12.908117
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发表时间：
2012-01
期刊：
影响因子：
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通讯作者：
Chongke Bi;Shigeo Takahashi;I. Fujishiro
中科院分区：
文献类型：
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作者：
Chongke Bi;Shigeo Takahashi;I. Fujishiro
3D 扩散张量场的可视化分析已成为一个重要课题，特别是在医学成像领域，用于了解生物组织的微观结构和物理特性。然而，由于缺乏适当的插值方案，从离散张量样本中连续跟踪底层特征仍然很困难，因为我们能够在充分尊重张量各向异性特征的平滑过渡的同时处理可能的简并性。这是因为简并性可能会导致张量各向异性的旋转不一致。本文提出了一种插值 3D 扩散张量场的方法。我们的方法背后的主要思想是通过分析相邻张量之间的相关特征结构来优化一对相邻张量之间的旋转变换来解决可能的简并性，而简并性可以通过对原始张量样本应用基于最小生成树的聚类算法来识别。将提供与现有插值方案的比较，以证明我们的方案的优点，以及跟踪人脑中白质纤维束的几个结果。
Visual analysis of 3D diffusion tensor fields has become an important topic especially in medical imaging for understanding microscopic structures and physical properties of biological tissues. However, it is still difficult to continuously track the underlying features from discrete tensor samples, due to the absence of appropriate interpolation schemes in the sense that we are able to handle possible degeneracy while fully respecting the smooth transition of tensor anisotropic features. This is because the degeneracy may cause rotational inconsistency of tensor anisotropy. This paper presents such an approach to interpolating 3D diffusion tensor fields. The primary idea behind our approach is to resolve the possible degeneracy through optimizing the rotational transformation between a pair of neighboring tensors by analyzing their associated eigenstructure, while the degeneracy can be identified by applying a minimum spanning tree-based clustering algorithm to the original tensor samples. Comparisons with existing interpolation schemes will be provided to demonstrate the advantages of our scheme, together with several results of tracking white matter fiber bundles in a human brain.