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: 7956296
• 负责人: Andriy Fedorov
• 金额: $ 0.08万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7956296
• 关键词: Algorithms; Arts; Biomedical Research; Brain; Brain scan; Case Study; Computer Retrieval of Information on Scientific Projects Database; Data Set; Funding; Grant; High Performance Computing; Image; Image Analysis; Institution; Magnetic Resonance Imaging; Medical Imaging; Methods; Outcome; Pilot Projects; Procedures; Process; Research; Research Personnel; Resolution; Resources; Source; Speed; Time; United States National Institutes of Health; 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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 在这项初步研究中，我们将评估TeraGrid在医学图像处理和分析方面的适用性。具体地说，我们将研究使用TeraGrid进行大规模参数研究和匿名MRI脑扫描的离线处理。我们特别感兴趣的是研究最先进的脑MRI配准和分割算法。众所周知，现有方法很难调整，并且可能需要大量时间来处理真实的高分辨率MRI数据集。我们将首先将我们实验室开发的软件以及其他图像处理工具移植到TeraGrid上。接下来，我们将使用SWIFT Engine(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，这可能导致改进的算法，更好的准确性，并从长远来看，改善图像引导程序的结果。