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细胞自动机束追踪:GPU 上的快速测地线扩散磁共振束追踪和基于连通性的分割。

Cellular Automata Tractography: Fast Geodesic Diffusion MR Tractography and Connectivity Based Segmentation on the GPU.

机构信息

Faculty of Engineering, Department of Biomedical Engineering, Yeditepe University, Istanbul, Turkey.

出版信息

Neuroinformatics. 2020 Jan;18(1):25-41. doi: 10.1007/s12021-019-09425-y.

Abstract

Geodesic based tractography on diffusion magnetic resonance data is a method to devise long distance connectivities among the brain regions. In this study, cellular automata technique is applied to the geodesic tractography problem and the algorithm is implemented on a graphics processing unit. Cellular automaton based method is preferable to current techniques due to its parallel nature and ability to solve the connectivity based segmentation problem with the same computational complexity, which has important applications in neuroimaging. An application to prior-less tracking and connectivity based segmentation of corpus callosum fibers is presented as an example. A geodesic tractography based corpus callosum atlas is provided, which reveals high projections to the cortical language areas. The developed method not only allows fast computation especially for segmentation but also provides a powerful and intuitive framework, suitable to derive new algorithms to perform connectivity calculations and allowing novel applications.

摘要

基于测地线的扩散磁共振数据轨迹描绘是一种设计大脑区域之间远距离连通性的方法。在这项研究中,元胞自动机技术被应用于测地线轨迹描绘问题,并且该算法在图形处理单元上实现。基于元胞自动机的方法由于其并行性质和能够以相同的计算复杂度解决基于连通性的分割问题,因此优于当前技术,这在神经影像学中有重要应用。作为一个示例,提出了一种无先验的跟踪和基于连通性的胼胝体纤维分割的应用。提供了一个基于测地线轨迹描绘的胼胝体图谱,揭示了与皮质语言区域的高投射。所开发的方法不仅允许快速计算,特别是用于分割,而且还提供了一个强大而直观的框架,适合衍生新的算法来执行连通性计算,并允许新的应用。

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