Department of Computer Information Systems, Al-Balqa Applied University, Al Salt 19117, Jordan.
IEEE Trans Biomed Eng. 2012 Sep;59(9):2594-603. doi: 10.1109/TBME.2012.2207898. Epub 2012 Jul 11.
Recent advances in programming languages for graphics processing units (GPUs) provide developers with a convenient way of implementing applications which can be executed on the CPU and GPU interchangeably. GPUs are becoming relatively cheap, powerful, and widely available hardware components, which can be used to perform intensive calculations. The last decade of hardware performance developments shows that GPU-based computation is progressing significantly faster than CPU-based computation, particularly if one considers the execution of highly parallelisable algorithms. Future predictions illustrate that this trend is likely to continue. In this paper, we introduce a way of accelerating 2-D/3-D image registration by developing a hybrid system which executes on the CPU and utilizes the GPU for parallelizing the generation of digitally reconstructed radiographs (DRRs). Based on the advancements of the GPU over the CPU, it is timely to exploit the benefits of many-core GPU technology by developing algorithms for DRR generation. Although some previous work has investigated the rendering of DRRs using the GPU, this paper investigates approximations which reduce the computational overhead while still maintaining a quality consistent with that needed for 2-D/3-D registration with sufficient accuracy to be clinically acceptable in certain applications of radiation oncology. Furthermore, by comparing implementations of 2-D/3-D registration on the CPU and GPU, we investigate current performance and propose an optimal framework for PC implementations addressing the rigid registration problem. Using this framework, we are able to render DRR images from a 256×256×133 CT volume in ~24 ms using an NVidia GeForce 8800 GTX and in ~2 ms using NVidia GeForce GTX 580. In addition to applications requiring fast automatic patient setup, these levels of performance suggest image-guided radiation therapy at video frame rates is technically feasible using relatively low cost PC architecture.
近年来，图形处理单元（GPU）编程语言的进步为开发人员提供了一种方便的方法，可以实现可以在 CPU 和 GPU 上交替执行的应用程序。GPU 变得相对便宜、功能强大且广泛可用，可用于执行密集型计算。过去十年的硬件性能发展表明，基于 GPU 的计算进展明显快于基于 CPU 的计算，尤其是在考虑高度可并行化算法的执行时。未来的预测表明，这种趋势可能会持续下去。在本文中，我们通过开发一种在 CPU 上执行并利用 GPU 来并行生成数字重建射线照片（DRR）的混合系统，介绍了一种加速 2D/3D 图像配准的方法。基于 GPU 相对于 CPU 的优势，现在是时候通过开发用于 DRR 生成的算法来利用多核 GPU 技术的优势了。尽管之前的一些工作已经研究了使用 GPU 渲染 DRR，但本文研究了在保持与 2D/3D 配准所需的质量一致的同时，减少计算开销的近似值，以在某些放射肿瘤学应用中达到足够的准确性，使其在临床中可以接受。此外，通过比较在 CPU 和 GPU 上实现 2D/3D 配准，我们研究了当前的性能，并提出了一种针对 PC 实现的最佳框架，用于解决刚体配准问题。使用这个框架，我们能够在大约 24 毫秒内使用 NVidia GeForce 8800 GTX 从 256×256×133 CT 体渲染 DRR 图像，而在大约 2 毫秒内使用 NVidia GeForce GTX 580。除了需要快速自动患者设置的应用程序外，这些性能水平表明，使用相对低成本的 PC 架构，在视频帧率下进行图像引导的放射治疗在技术上是可行的。
