A CASE STUDY OF USING TERAGRID FOR LARGE-SCALE MEDICAL IMAGE ANALYSIS

使用 teragrid 进行大规模医学图像分析的案例研究

• 批准号: 8364166
• 负责人: Andriy Fedorov
• 金额: $ 0.11万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8364166
• 关键词: Algorithms; Biomedical Research; Brain; Brain scan; Case Study; Data Set; Funding; Grant; High Performance Computing; Image; Image Analysis; Magnetic Resonance Imaging; Medical Imaging; Methods; National Center for Research Resources; Outcome; Pilot Projects; Principal Investigator; Procedures; Process; Research; Research Infrastructure; Resolution; Resources; Source; Speed; Time; United States National Institutes of Health; cost; image processing; improved; interest; software development; tool

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. In this pilot study we will evaluate the applicability of TeraGrid for the purposes of medical image processing and analysis. Specifically, we will study the use of TeraGrid for conducting large-scale parametric studies and off-line processing of anonymized MRI brain scans. We are particularly interested in studying state of the art algorithms for registration and segmentation of brain MRI. Existing methods are known to be difficult to tune, and may require extensive times for processing realistic high-resolution MRI datasets. We will first port software developed in our lab, together with the other image processing tools, on TeraGrid. Next, we will use Swift engine (http://www.ci.uchicago.edu/swift/) to describe and execute typical workflows and parametric studies of our interest. The potential of this study is to simplify and speed up analysis of medical images, which may result in improved algorithms, better accuracy, and improved outcomes of image-guided procedures in the long term.

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其他NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 在这项试点研究中，我们将评估TeraGrid的医学图像处理和分析的目的的适用性。具体来说，我们将研究使用TeraGrid进行大规模参数研究和离线处理匿名MRI脑部扫描。我们特别感兴趣的是研究最先进的算法配准和分割的大脑MRI。已知现有方法难以调谐，并且可能需要大量时间来处理真实的高分辨率MRI数据集。我们将首先在TeraGrid上移植我们实验室开发的软件以及其他图像处理工具。接下来，我们将使用Swift引擎（http：//www.ci.uchicago.edu/swift/）来描述和执行我们感兴趣的典型工作流程和参数研究。这项研究的潜力是简化和加快医学图像的分析，这可能会导致改进的算法，更好的准确性，并改善图像引导程序的长期结果。