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

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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.