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使用三角形带和点精灵对白质束进行混合可视化。

Hybrid visualization for white matter tracts using triangle strips and point sprites.

作者信息

Merhof Dorit, Sonntag Markus, Enders Frank, Nimsky Christopher, Hastreiter Peter, Greiner Günther

机构信息

Computer Graphics Group, Univ. Erlangen.

出版信息

IEEE Trans Vis Comput Graph. 2006 Sep-Oct;12(5):1181-8. doi: 10.1109/TVCG.2006.151.

Abstract

Diffusion tensor imaging is of high value in neurosurgery, providing information about the location of white matter tracts in the human brain. For their reconstruction, streamline techniques commonly referred to as fiber tracking model the underlying fiber structures and have therefore gained interest. To meet the requirements of surgical planning and to overcome the visual limitations of line representations, a new real-time visualization approach of high visual quality is introduced. For this purpose, textured triangle strips and point sprites are combined in a hybrid strategy employing GPU programming. The triangle strips follow the fiber streamlines and are textured to obtain a tube-like appearance. A vertex program is used to orient the triangle strips towards the camera. In order to avoid triangle flipping in case of fiber segments where the viewing and segment direction are parallel, a correct visual representation is achieved in these areas by chains of point sprites. As a result, a high quality visualization similar to tubes is provided allowing for interactive multimodal inspection. Overall, the presented approach is faster than existing techniques of similar visualization quality and at the same time allows for real-time rendering of dense bundles encompassing a high number of fibers, which is of high importance for diagnosis and surgical planning.

摘要

扩散张量成像在神经外科手术中具有很高的价值,它能提供有关人脑白质束位置的信息。对于白质束的重建,通常称为纤维追踪的流线技术对潜在的纤维结构进行建模,因此受到了关注。为了满足手术规划的要求并克服线条表示的视觉局限性,引入了一种具有高视觉质量的新实时可视化方法。为此,采用GPU编程的混合策略将纹理三角形带和点精灵相结合。三角形带沿着纤维流线排列,并添加纹理以获得类似管道的外观。顶点程序用于将三角形带朝向相机定向。为了避免在观察方向与纤维段方向平行的纤维段情况下三角形翻转,通过点精灵链在这些区域实现正确的视觉表示。结果,提供了一种类似于管道的高质量可视化,允许进行交互式多模态检查。总体而言,所提出的方法比具有类似可视化质量的现有技术更快,同时允许对包含大量纤维的密集束进行实时渲染,这对于诊断和手术规划非常重要。

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