Difference between revisions of "Collaboration:mBIRN"
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==Grant#== | ==Grant#== | ||
| − | * | + | *U24RR021382 |
==Key Personnel== | ==Key Personnel== | ||
| − | * | + | *mBIRN: Bruce Rosen, MGH |
*NA-MIC: Ron Kikinis, Steve Pieper | *NA-MIC: Ron Kikinis, Steve Pieper | ||
==Grant Duration== | ==Grant Duration== | ||
| − | + | 09/30/2004-05/31/2010 | |
==Grant Abstract== | ==Grant Abstract== | ||
| − | DESCRIPTION (from NIH Reporter): The | + | DESCRIPTION (from NIH Reporter): Technological advances in imaging have revolutionized the biomedical investigation of illness. The tremendous potential that this methodology brings to advancing diagnostic and prognostic capabilities and in treatment of illnesses has as yet remained largely an unfulfilled promise. This potential has been limited by a number of technological impediments that could be in large part overcome by the availability of a federated imaging database and the attendant infrastructure. Specifically, the ability to conduct clinical imaging studies across multiple sites, to analyze imaging data with the most powerful software regardless of development site, and to test new hypotheses on large collections of subjects with well characterized image and clinical data would have a demonstrable and positive impact on progress in this field. The Morphometry BIRN (mBIRN), established in October 2001, has made substantial progress in the development of this national infrastructure to develop a data and computational network based on a federated data acquisition and database across seven sites in the service of facilitating multi-site neuroanatomic analysis. Standardized structural MRI image acquisition protocols have been developed and implemented that demonstrably reduce initial sources of inter-site variance. Data structure, transmission, storage and querying aspects of the federated database have been implemented. In this continuation of the mBIRN efforts, we propose three broad areas of work: 1) continuing structural MRI acquisition optimization, calibration and validation to include T2 and DTI; 2) translation of site specific state-of-the-art image analysis, visualization and machine learning technologies to work in the federated, multi-site BIRN environment; and 3) extension of data management and database query capabilities to include additional imaging modalities, clinical disorders and individualized human genetic covariates. These broad areas of work will come together in through key collaborations that will ensure utilization promotion by facilitating data entry into the federated database and creation of database incentive functionality. Our participating sites include MGH (PI), BWH, UCI, Duke, UCLA, UCSD, John Hopkins, and newly added Washington University and MIT. We have made a concerted effort to bridge the gap that can exist between biomedical and computational sciences by recruiting to our group leaders in both of these domains. Our efforts will be coordinated with those of the entire BIRN consortium in order to insure that acquisition and database functionality, and application-based disorder queries are interoperable across sites and designed to advance the capabilities to further knowledge and understanding of health and disease. |
Latest revision as of 14:49, 10 February 2010
Home < Collaboration:mBIRNBack to NA-MIC External Collaborations
Grant#
- U24RR021382
Key Personnel
- mBIRN: Bruce Rosen, MGH
- NA-MIC: Ron Kikinis, Steve Pieper
Grant Duration
09/30/2004-05/31/2010
Grant Abstract
DESCRIPTION (from NIH Reporter): Technological advances in imaging have revolutionized the biomedical investigation of illness. The tremendous potential that this methodology brings to advancing diagnostic and prognostic capabilities and in treatment of illnesses has as yet remained largely an unfulfilled promise. This potential has been limited by a number of technological impediments that could be in large part overcome by the availability of a federated imaging database and the attendant infrastructure. Specifically, the ability to conduct clinical imaging studies across multiple sites, to analyze imaging data with the most powerful software regardless of development site, and to test new hypotheses on large collections of subjects with well characterized image and clinical data would have a demonstrable and positive impact on progress in this field. The Morphometry BIRN (mBIRN), established in October 2001, has made substantial progress in the development of this national infrastructure to develop a data and computational network based on a federated data acquisition and database across seven sites in the service of facilitating multi-site neuroanatomic analysis. Standardized structural MRI image acquisition protocols have been developed and implemented that demonstrably reduce initial sources of inter-site variance. Data structure, transmission, storage and querying aspects of the federated database have been implemented. In this continuation of the mBIRN efforts, we propose three broad areas of work: 1) continuing structural MRI acquisition optimization, calibration and validation to include T2 and DTI; 2) translation of site specific state-of-the-art image analysis, visualization and machine learning technologies to work in the federated, multi-site BIRN environment; and 3) extension of data management and database query capabilities to include additional imaging modalities, clinical disorders and individualized human genetic covariates. These broad areas of work will come together in through key collaborations that will ensure utilization promotion by facilitating data entry into the federated database and creation of database incentive functionality. Our participating sites include MGH (PI), BWH, UCI, Duke, UCLA, UCSD, John Hopkins, and newly added Washington University and MIT. We have made a concerted effort to bridge the gap that can exist between biomedical and computational sciences by recruiting to our group leaders in both of these domains. Our efforts will be coordinated with those of the entire BIRN consortium in order to insure that acquisition and database functionality, and application-based disorder queries are interoperable across sites and designed to advance the capabilities to further knowledge and understanding of health and disease.
