HIGH PERFORMANCE COMPUTING (HPC): CLINICAL APPLICATION & MEDICAL IMAGE ANALYSIS

该子提案的目的有两个：开发HPC 实现经过验证的分割、配准和可视化 技术;开发新的图像采集，分割， 使用HPC作为支持的注册和可视化功能 技术. 高性能计算将用于NAC， 和/或基于大图像的数据集的交互式分析。 具体而言：1. 我们将应用并行算法的效用 并利用专用的超级计算硬件（MPP、SMP和 集群机器）来设计和开发一些应用程序， 现在已经在临床上使用了。 2. 我们将利用最新的硬件 以及软件技术的发展， 价格昂贵且更容易应用于医学图像分析问题。 我们 我将利用这些技术进步， 多处理器和群集优化的图像应用程序 采集、分割、配准和可视化。 这些 实现将实现高性能，高度便携， 易于传播。

The aim of this sub-proposal is twofold: to develop HPC implementations of proven segmentation, registration and visualization techniques; to develop new image acquisition, segmentation, registration and visualization capabilities using HPC as an enabling technology. High Performance Computing will be used in the NAC to drive rapid and/or interactive analysis of large image based data-sets. Specifically: 1. We will apply the utility of parallel algorithms and make use of dedicated supercomputing hardware (MPP, SMP and cluster machines) to design and develop a number of applications which are now in routine clinical use. 2. We will utilize recent hardware and software technology developments which have made HPC much less expensive and easier to apply to medical image analysis problem. We will exploit these technology improvements by developing symmetric multi-processor and cluster optimized applications for image acquisition, segmentation, registration and visualization. These implementations will achieve high performance, be highly portable and easy to disseminate.