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This case study should resonate with anyone frustrated by trying to use a DICOM PACS database to manage their research.Says David Gutman, "Instead of having to conform to the PACS data, I can make the data conform to me."
David Gutman, M.D. Ph.D., works at the Center for Comprehensive Informatics at Emory University, where he has spent time studying brain connectivity and other forays into neuroimaging-based research. As one example, exploring how the structure and anatomy of the brain might correlate to clinically diagnosed neuropsychological conditions such as depression or PTSD. Another field of research might involve isolating and comparing brain tumor tissues such as glioblastomas across multiple patients and correlating tissue composition with known clinical outcomes.
Like many others in the field, Gutman's research depends on data mining imaging studies involving large groups of people, and making probabilistic comparisons using data processing pipelines such as the LONI pipeline. Dr. Gutman's pipelines generate multiple statistical maps that need to be stored and associated with his imaging data and this functionality just doesn't exist with a traditional PACS. The alternative, storing millions of files on his local file system, was equally unpalatable.
For Gutman, XNAT provides:
Additionally, XNAT serves as a data backbone for advanced informatics exploration tools that Gutman is currently developing.
Currently, only Gutman, his co-PI, and their group of assistants are using XNAT. However, future projects on deck include a large study being coordinated through his department that will involve multiple investigators and neuroradiologists tracking a number of subjects through multiple visits. XNAT will also prove valuable when it comes time to share and synchronize his completed data with other investigators.
Says Gutman: "A year ago, I didn't know what REST was. Now, I love REST." XNAT's REST-based data access provides incredible flexibility in working with his data, pulling from the database for processing and pushing resulting data sets back to the DB.
Only minor customizations so far, adding custom variables to support specific data captured as part of the research process. Planning to experiment with extending the XNAT schema for subjects and tumor studies.
Would love more scripts and more supported processes. And would appreciate more documentation [Ed: we're working on it!], providing concrete examples and best practices for using XNAT.