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Large-scale Parallel Katsevich Algorithm for 3D Cone-beam CT Image Reconstruction

3D锥束CT图像重建的大规模并行Katsevich算法

基本信息

批准号：
7131045
负责人：
Jun Ni
金额：
$ 22.13万
依托单位：
UNIVERSITY OF IOWA
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-07-01 至 2008-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7131045
关键词：
communication computers performance

项目摘要

DESCRIPTION (provided by applicant): Medical image reconstruction requires high performance computing (HPC) and high-end computing resources. They are extemely critical for practical implementation of cutting-edge technology in CT/micro-CT medical imaging. Although the recently developed algorithms are very sophisticated, they require significant time for 3-D image reconstruction. It has been a challenge for decades to find an economic and efficient parallel algorithm, and high performance computing system. The recently developed parallel Katsevich algorithm for 3D cone-beam CT image reconstruction at the University of Iowa has prompted the investigators to develop a large-scale parallel Katsevich algorithm. This algorithm will be developed and implemented for high-resolution CT/micro-CT medical image reconstructions using NSF TeraGrid system which integrates a massive number of processors. The overall goal of this proposal is to develop a specific parallel algorithm for 3-D CT/micro-CT medical image reconstruction on large scale heterogeneous systems. This parallel algorithm will allow medical researchers and/or clinical professionals to achieve high-performance for high-resolution, 3-D medical image reconstruction on a large-scale distributed computing system integrating multiple HPC clusters. The specific aims of this R21 project are to (1) develop large-scale parallel Katsevich algorithm on high performance computing systems with focuses on memory allocation, projection data decomposition, and scalability; (2) develop functions which can account for load balancing, fault-tolerance, and network impact in distributed environment; (3) compute benchmarks for evaluation of parallel performance in terms of speed-up, parallel efficiency, scalability, granularity, and network latency, using TeraGrid supercomputing resources.

描述（由申请人提供）：医学图像重建需要高性能计算（HPC）和高端计算资源。它们对于CT/micro-CT医学成像中尖端技术的实际实施至关重要。虽然最近开发的算法是非常复杂的，他们需要显着的时间为3-D图像重建。寻找一种经济高效的并行算法和高性能的计算系统一直是一个挑战。爱荷华州大学最近开发的用于3D锥束CT图像重建的并行Katsevich算法促使研究人员开发大规模并行Katsevich算法。该算法将开发和实现高分辨率CT/微型CT医学图像重建使用NSF TeraGrid系统，它集成了大量的处理器。本提案的总体目标是开发一个特定的并行算法，用于大规模异构系统上的3-D CT/micro-CT医学图像重建。这种并行算法将允许医学研究人员和/或临床专业人员在集成多个HPC集群的大规模分布式计算系统上实现高性能的高分辨率、3-D医学图像重建。该R21项目的具体目标是：（1）在高性能计算系统上开发大规模并行Katsevich算法，重点关注内存分配，投影数据分解和可扩展性;（2）开发可以在分布式环境中考虑负载平衡，容错和网络影响的功能;（3）使用TeraGrid超级计算资源，从加速、并行效率、可扩展性、粒度和网络延迟等方面计算用于评估并行性能的基准。