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首页> 外文期刊>IEEE transactions on visualization and computer graphics >Interactive Visualization of Volumetric White Matter Connectivity in DT-MRI Using a Parallel-Hardware Hamilton-Jacobi Solver
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Interactive Visualization of Volumetric White Matter Connectivity in DT-MRI Using a Parallel-Hardware Hamilton-Jacobi Solver

机译：使用并行硬件Hamilton-Jacobi解算器的DT-MRI中体积白质连通性的交互式可视化

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摘要

In this paper we present a method to compute and visualize volumetric white matter connectivity in diffusion tensor magnetic resonance imaging (DT-MRI) using a Hamilton-Jacobi (H-J) solver on the GPU (Graphics Processing Unit). Paths through the volume are assigned costs that are lower if they are consistent with the preferred diffusion directions. The proposed method finds a set of voxels in the DTI volume that contain paths between two regions whose costs are within a threshold of the optimal path. The result is a volumetric optimal path analysis, which is driven by clinical and scientific questions relating to the connectivity between various known anatomical regions of the brain. To solve the minimal path problem quickly, we introduce a novel numerical algorithm for solving H-J equations, which we call the Fast Iterative Method (FIM). This algorithm is well-adapted to parallel architectures, and we present a GPU-based implementation, which runs roughly 50-100 times faster than traditional CPU-based solvers for anisotropic H-J equations. The proposed system allows users to freely change the endpoints of interesting pathways and to visualize the optimal volumetric path between them at an interactive rate. We demonstrate the proposed method on some synthetic and real DT-MRI datasets and compare the performance with existing methods.
机译：在本文中，我们介绍了一种在GPU（图形处理单元）上使用Hamilton-Jacobi（H-J）求解器在扩散张量磁共振成像（DT-MRI）中计算和可视化体积白质连通性的方法。如果通过量的路径与首选的扩散方向一致，则分配的成本较低。所提出的方法在DTI​​体积中找到一组体素，这些体素包含两个区域之间的路径，这些区域的成本在最佳路径的阈值内。结果是通过与大脑各种已知解剖区域之间的连通性有关的临床和科学问题驱动的体积最佳路径分析。为了快速解决最小路径问题，我们引入了一种新型的求解H-J方程的数值算法，称为快速迭代法（FIM）。该算法非常适合于并行体系结构，并且我们提出了一种基于GPU的实现，该算法的运行速度比传统基于CPU的各向异性H-J方程的求解器快50-100倍。提出的系统允许用户自由更改有趣路径的端点，并以交互速率可视化它们之间的最佳体积路径。我们在一些合成的和真实的DT-MRI数据集上演示了该方法，并将其性能与现有方法进行了比较。

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