Studies of PC cluster-based parallel processing for large-scale medical images on navigation system of the next generation surgery

在医学图像处理中，以下两个问题是非常重要的。本文针对这些问题开发了基于PC集群的并行程序，该PC集群上安装了PC，每台PC上都有两个2 GB主存的奔腾-3(1 GHz)处理器，并与Myrinet2000通信网络相连。(1)体绘制：为了实时绘制至少512x512x512体素体，我们开发了一种基于排序的并行体绘制方法fQR分布式内存多处理器。我们的最后排序方法包括两种方法，徐氏S分段光线投射和我们的分割屏幕分层合成，在这两种方法中，每个处理器产生一个子图像，并将产生的所有子图像合并到最终图像中。本文描述了DSH方法，它的目标是在大量处理器上实现高性能的合成：我们在PC集群上实现的合成512 x 512像素图像的速度大约是现有方法二进制交换方法的两倍，从而可以以大约8帧每秒(Fps)的速度绘制512 x 512 x 224体素的体积。(2)无刚性图像配准：我们的算法通过三种技术实现了高分辨率三维图像的可伸缩配准：(1)数据分布；(2)数据并行处理；(3)动态负载平衡。实验结果表明，我们在PC集群上的并行实现在8分钟内配准了512×512×159体素的肝脏CT图像，而顺序实现则需要12小时。此外，我们的实现允许处理器使用更少的内存，从而使我们能够对齐1024×1024×590体素图像，由于内存空间和处理时间的限制，这对于单处理器系统来说并不容易。

The following two problems are very important in medical image processing. In this study, PC cluster-based parallel programs for these problems are developed, This PC cluster has 64 PCs, each of which has two Pentium-3 (1GHz) processors with 2GB main memory, connected with Myrinet 2000 communication network.(1) Volume rendering:To render at least 512 x 512 x 512 voxel volumes in real-time, we have developed a sort-last parallel volume rendering method fqr distributed memory multiprocessors. Our sort-last method consists of two methods, Hsu' s segmented ray casting and our divided-screenwise hierarchical (DSH) composition, in which each processor produces a subimage and merges all the produced subimages into the final image. This paper describes the DSH method, which aims at achieving high performance composition on a large number of processors: Ourimplementation on the PC cluster can composite a 512 x 512 pixel image about twice as fast as an existing method, the binary-swap method, so that can render a 512 x 512 x 224 voxel volume at approximately eight frames per second (fps).(2) No rigid image registration:Our algorithm realizes scalable registration for high-resolution three-dimensional (3-D) images by employing three techniques: (1) data distribution; (2) data-parallel processing; and (3) dynamic load balancing. The experimental results show that our parallel implementation on the PC cluster registers liver CT images of 512 X 512 X 159 voxels within 8 minutes while a sequential implementation takes 12 hours. Furthermore, our implementation allows processors to use less memory, and thereby enables us to align 1024 X 1024 X 590 voxel images, which is not easy for single processor systems due to the restrictions on the memory space and the processing time.