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

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 中心，不一定是研究者的机构。 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。

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.