Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation.

IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium Pub Date : 2014-11-01 DOI:10.1109/NSSMIC.2014.7430758

Jae H Lee, Yushu Yao, Uttam Shrestha, Grant T Gullberg, Youngho Seo

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引用次数: 3

Abstract

The primary goal of this project is to implement the iterative statistical image reconstruction algorithm, in this case maximum likelihood expectation maximum (MLEM) used for dynamic cardiac single photon emission computed tomography, on Spark/GraphX. This involves porting the algorithm to run on large-scale parallel computing systems. Spark is an easy-to- program software platform that can handle large amounts of data in parallel. GraphX is a graph analytic system running on top of Spark to handle graph and sparse linear algebra operations in parallel. The main advantage of implementing MLEM algorithm in Spark/GraphX is that it allows users to parallelize such computation without any expertise in parallel computing or prior knowledge in computer science. In this paper we demonstrate a successful implementation of MLEM in Spark/GraphX and present the performance gains with the goal to eventually make it useable in clinical setting.

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医学成像中的大数据处理:大规模自动并行计算的迭代重建。

该项目的主要目标是在Spark/GraphX上实现迭代统计图像重建算法，在这种情况下，最大似然期望最大值(MLEM)用于动态心脏单光子发射计算机断层扫描。这涉及到将算法移植到大型并行计算系统上运行。Spark是一个易于编程的软件平台，可以并行处理大量数据。GraphX是一个运行在Spark之上的图形分析系统，用于并行处理图形和稀疏线性代数运算。在Spark/GraphX中实现MLEM算法的主要优点是，它允许用户在没有并行计算专业知识或计算机科学先验知识的情况下并行化此类计算。在本文中，我们展示了在Spark/GraphX中成功实现的MLEM，并展示了性能收益，目标是最终使其可用于临床环境。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium

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