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1 From the Department of Biomedical Informatics, Ohio State University, 3190 Graves Hall, 333 W 10th Ave, Columbus, OH 43210 (T.C.P., M.N.G., S.A.L., S.W.O., S.L.H., A.S., B.G.R., D.W.E., T.M.K., J.H.S.); VA Maryland Health Care System, Baltimore, Md (K.M.S., E.L.S.); and University of Maryland School of Medicine, Baltimore, Md (E.L.S.). Presented as an infoRAD exhibit at the 2005 RSNA Annual Meeting. Received August 17, 2006; revision requested September 22 and received November 8; accepted December 20. Supported in part by the National Cancer Institute, the National Science Foundation (CNS-0509326, CNS-0403342, ANI-0330612), the National Institutes of Health (NIBIB BISTI P20EB000591), and the Ohio Board of Regents (BRTTC BRTT02– 0003, ODOD-AGMT-TECH-04 – 049). M.N.G. is a stockholder in iCAD. K.M.S. is a speaker for TeraRecon, San Mateo, Calif; cofounder of iVirtuoso, Baltimore, Md; and a member of the advisory board of GE Healthcare IT, Barrington, Ill. E.L.S. received research funding from GE Healthcare. All other authors have no financial relationships to disclose. Address correspondence to T.C.P. (e-mail: tpan{at}bmi.osu.edu).
Grid computing—the use of a distributed network of electronic resources to cooperatively perform subsets of computationally intensive tasks—may help improve the speed and accuracy of radiologic image interpretation by enabling collaborative computer-based and human readings. GridCAD, a software application developed by using the National Cancer Institute Cancer Biomedical Informatics Grid architecture, implements the fundamental elements of grid computing and demonstrates the potential benefits of grid technology for medical imaging. It allows users to query local and remote image databases, view images, and simultaneously run multiple computer-assisted detection (CAD) algorithms on the images selected. The prototype CAD systems that are incorporated in the software application are designed for the detection of lung nodules on thoracic computed tomographic images. GridCAD displays the original full-resolution images with an overlay of nodule candidates detected by the CAD algorithms, by human observers, or by a combination of both types of readers. With an underlying framework that is computer platform independent and scalable to the task, the software application can support local and long-distance collaboration in both research and clinical practice through the efficient, secure, and reliable sharing of resources for image data mining, analysis, and archiving.
© RSNA, 2007
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