Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware
Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware
复制标题
DOI：
10.1109/tns.2005.851398
复制
发表时间：
2005-06-01
影响因子：
1.8
通讯作者：
Mueller, K
中科院分区：
文献类型：
--
作者：
Xu, F;Mueller, K
由于数据的巨大复杂性，通过断层摄影方法从其投影重建对象的任务是一个耗时的过程。出于这个原因，用于医疗计算机断层扫描（CT）的设备的制造商主要依赖于专用集成电路（ASIC）来获得临床环境中所需的快速重建时间。虽然现代CPU近年来已经获得了足够的能力来竞争二维（2D）重建，但这不是三维（3D）重建的情况，特别是当必须应用迭代算法时。商品PC计算机图形板（GPU）的最新发展有可能以非常戏剧性的方式改变这一局面。在本文中，我们将展示如何利用新的浮点GPU来执行X射线和功能成像数据的分析和迭代重建。为此，我们将三种流行的三维（3D）重建算法（Feldkamp滤波反投影，同时代数重建技术和期望最大化）分解为一组通用的基本模块，这些模块都可以在GPU上执行，并且它们的输出在内部链接。重建对象的可视化很容易实现，因为对象已经驻留在图形硬件中，允许人们在任何时候运行可视化模块来查看重建结果。我们的实现允许加速超过一个数量级的CPU实现，在可比的图像质量。
The task of reconstructing an object from its projections via tomographic methods is a time-consuming process due to the vast complexity of the data. For this reason, manufacturers of equipment for medical computed tomography (CT) rely mostly on special application specified integrated circuits (ASICs) to obtain the fast reconstruction times required in clinical settings. Although modern CPUs have gained sufficient power in recent years to be competitive for two-dimensional (2D) reconstruction, this is not the case for three-dimensional (3D) reconstructions, especially not when iterative algorithms must be applied. The recent evolution of commodity PC computer graphics boards (GPUs) has the potential to change this picture in a very dramatic way. In this paper we will show how the new floating point GPUs can be exploited to perform both analytical and iterative reconstruction from X-ray and functional imaging data. For this purpose, we decompose three popular three-dimensional (3D) reconstruction algorithms (Feldkamp filtered backprojection, the simultaneous algebraic reconstruction technique, and expectation maximization) into a common set of base modules, which all can be executed on the GPU and their output linked internally. Visualization of the reconstructed object is easily achieved since the object already resides in the graphics hardware, allowing one to run a visualization module at any time to view the reconstruction results. Our implementation allows speedups of over an order of magnitude with respect to CPU implementations, at comparable image quality.