基于异构多GPU的锥束CT图像重建研究

针对锥束CT图像重建系统中GPU型号不一致问题，提出了基于异构多GPU的重建模型。该模型基于FDK算法进行重建，采用了按计算能力进行任务分配的方法，确保各GPU计算平衡。采用数据流分解的方法，实现了海量数据的图像重建。给出了该重建模型基于CUDA的实现方法，包括采用流管理和异步函数来实现多GPU并行计算以及滤波和反投影核函数的流程设计。利用高精度工业CT系统进行模型的实验验证。结果表明：所建立的重建模型正确有效，能充分发挥系统中异构多GPU的计算能力，执行效率高。

Research on Cone-beam Computed Tomographic Reconstruction Based on Heterogeneous Multi-GPU

With respect to the problem due to the different multi-GPU types in cone-beam CT reconstruction, a model was proposed based on heterogeneous multi-GPUs. Using FDK algorithm for reconstruction, the model allocated tasks according to the computing capacity of each GPU, ensuring the balance among GPUs. Massive data image reconstruction was achieved by data flow decomposition. The implementation of the method was carried out based on CUDA, including multi-GPUs parallel computing using data flow management and asynchronous function and the design of the kernel function in filtering and back-projection. The model was tested on the high precision industrial CT system. The results illustrate that the reconstruction model is accurate and effective, taking full advantage of heterogeneous multi-GPUs, and is considerably effective compared to conventional methods.