Visualization of diffusion tensor data using evenly spaced streamlines
Visualization of diffusion tensor data using evenly spaced streamlines
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发表时间：
2005
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通讯作者：
D. Merhof;Markus Sonntag;Frank Enders;P. Hastreiter;R. Fahlbusch;C. Nimsky;G. Greiner
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作者：
D. Merhof;Markus Sonntag;Frank Enders;P. Hastreiter;R. Fahlbusch;C. Nimsky;G. Greiner
扩散张量成像可以研究体内白质结构，这对神经外科特别感兴趣。重建神经通路的一种有前景的方法是基于流线型的技术，通常称为纤维跟踪。然而，由于区域系统的发散性质，流线密度在不受控制的情况下在域内变化，导致稀疏区域和狭窄区域。为了克服这个问题，我们将均匀间隔流线的概念应用于光纤跟踪，提供在域上均匀分布的流线。此外，我们将均匀间隔的流线纳入基于感兴趣区域的跟踪中。我们还研究了根据各向异性扩散的大小对单独流线之间的距离进行自适应控制，这提供了一种强调主导束流系统的机制。
Diffusion tensor imaging allows investigating white matter structures in vivo which is of particular interest for neurosurgery. A promising approach for the reconstruction of neural pathways are streamline based techniques commonly referred to as fiber tracking. However, due to the diverging nature of tract systems, the density of streamlines varies over the domain without control resulting in sparse areas as well as cramped regions. To overcome this problem, we adapted the concept of evenly spaced streamlines to fiber tracking providing streamlines equally distributed over the domain. Additionally, we incorporated evenly spaced streamlines into region of interest based tracking. We also investigated an adaptive control of the distance between separate streamlines depending on the magnitude of anisotropic diffusion which provides a mechanism to emphasize dominant tract systems.