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of natural phenomenon. The aggregation of these data can have applications in a wide range of fields including law, education, business, and medicine.

There is an opportunity—and a need—to design imaging systems from the ground up, keeping both hardware and software in mind. The systems should facilitate the validation, preservation, and analysis of massive amounts of data. For example, the next generation of MR scanners should incorporate the software design team in the first stages of system planning, and the instruments should be engineered for the Exabyte scale. This type of engineering will require the cooperation of research scientists spanning the imaging community and software communities; these individuals typically have very different skill sets and are trained in different university or corporate programs.

Key Questions

  • What would it take to build a software infrastructure so that imaging systems developers can easily incorporate large-scale data sharing and data analysis, thereby enabling important information to be coordinated within/among a large user group?

  • Are there successful models, such as databases for face recognition and finger printing, that might be used as a model for other organizations, such as MR anatomical and functional data?

  • Are there common architectural and computational needs across multiple types of imaging modalities for storing, validating quality, and analyzing image databases? Are there general ontologies for imaging data that might be derived from the images themselves, rather than by labels added by the users in the metadata?


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Marcus DS, Archiw KA, Olsen TR, Ramarathnam M. The open-source neuroimaging research enterprise. J Digital Imaging epub 2001 Aug 21; Suppl 1:130-8. Accessed online June 15, 2010.

Small SL, Wilde M, Kenny S, Andric M, Hasson U. Database-managed grid-enabled analysis of neuroimaging data: the CNARI framework. Int J Psychophysiol epub 2009 Feb 20, 2009 Jul;73(1):62-72. Abstract accessed online June 15, 2010.

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