Probabilistic Tractography for Topographically Organized Connectomes.
Probabilistic Tractography for Topographically Organized Connectomes.
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DOI：
10.1007/978-3-319-46720-7_24
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发表时间：
2016-10-01
期刊：
影响因子：
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通讯作者：
Shi, Yonggang
中科院分区：
文献类型：
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作者：
Aydogan, Dogu Baran;Shi, Yonggang
虽然纤维束成像广泛用于脑成像研究，但其定量验证非常困难。然而，许多纤维系统具有众所周知的拓扑结构，甚至可以定量映射，例如视觉通路的视网膜病变。出于这种以前未开发的解剖学知识，我们开发了一种新的纤维束成像方法，保留了纤维系统的地形和几何规则性。对于地形保护，我们提出了一种新的似然函数，测试平行曲线和纤维取向分布之间的匹配。对于几何正则性，我们使用Frenet-Serret框架的高斯分布。总之，我们开发了一个贝叶斯框架，用于生成精确遵循神经解剖学的高度组织化的轨迹。使用多壳层扩散图像的56个主题从人类连接组计划，我们比较我们的方法与算法从MRP 10。通过应用回归分析之间的视网膜偏心率和轨道，我们定量地证明，我们的方法实现了上级性能，在保持视网膜组织的光辐射。
While tractography is widely used in brain imaging research, its quantitative validation is highly difficult. Many fiber systems, however, have well-known topographic organization which can even be quantitatively mapped such as the retinotopy of visual pathway. Motivated by this previously untapped anatomical knowledge, we develop a novel tractography method that preserves both topographic and geometric regularity of fiber systems. For topographic preservation, we propose a novel likelihood function that tests the match between parallel curves and fiber orientation distributions. For geometric regularity, we use Gaussian distributions of Frenet-Serret frames. Taken together, we develop a Bayesian framework for generating highly organized tracks that accurately follow neuroanatomy. Using multi-shell diffusion images of 56 subjects from Human Connectome Project, we compare our method with algorithms from MRtrix. By applying regression analysis between retinotopic eccentricity and tracks, we quantitatively demonstrate that our method achieves superior performance in preserving the retinotopic organization of optic radiation.