Four NIH Institutes (NICHD, NIMH, NINDS, and NIDA) jointly sponsored a multicenter study to advance our understanding about how the brain develops in typical, healthy children and adolescents. This study enrolled approximately 500 children, ranging from infancy to young adulthood, who were seen at different time points over a six year period (2001-2007) at several clinical centers around the U.S.. Magnetic Resonance Images (MRI) of the brain was performed and the imaging findings and standardized neuropsycological tests were administered. ? ? Our role has been to serve as a Diffusion Tensor Data Processing Center (DPC). This entails processing and analyzing all diffusion tensor MRI (DT-MRI or DTI) data that were acquired by the various clinical Centers during the course of the study. Previously, we have been developing and implementing the DTI data processing pipeline. Specifically we developed procedures for sorting, displaying, and co-registering diffusion weighted images (DWI) from which the DTI data is computed. The image registration procedure accomplishes the tasks of removing the effects of subject motion and eddy current distortion as well as aligning the images to a given template with only one interpolation step, ensuring minimal data quality loss. We also proposed a new strategy for robust estimation of the diffusion tensor and quantities derived from it. We addressed the task of registering DTI data to other structural MRI data contained in the database using rigid body and linear (affine) transformations. More recently we addressed the task of correcting residual image distortion originating from the Echo-Planar DWI (DW-EPI) acquisition used for DTI.? ? In 2005 the project was awarded Neuroscience Blueprint funds to expand the DTI portion of the study. The original DTI data collection was limited to only a 10 minute protocol performed on those subjects able to tolerate additional time in the scanner following the collection of the core structural MRI data. This recent expansion has improved the quality of data collected through longer or additional scanning sessions for older subjects, and through the recruitment of new subjects and scanning sessions for the younger cohort. Our group has designed the expanded DTI protocol and coordinated its implementation at each acquisition site. ? ? Clinical data collection was completed in August, 2007, however, structural MRI data that we need for coregistration with the diffusion data are still being processed by other investigators outside to our group. We plan to produce processed diffusion data that can be made available to the scientific community about a year after receiving all raw diffusion data and their corresponding structural targets.

Project Start
Project End
Budget Start
Budget End
Support Year
6
Fiscal Year
2008
Total Cost
$567,145
Indirect Cost
City
State
Country
United States
Zip Code
Koay, Cheng Guan; Ozarslan, Evren; Pierpaoli, Carlo (2009) Probabilistic Identification and Estimation of Noise (PIESNO): a self-consistent approach and its applications in MRI. J Magn Reson 199:94-103
Koay, Cheng Guan; Chang, Lin-Ching; Pierpaoli, Carlo et al. (2007) Error propagation framework for diffusion tensor imaging via diffusion tensor representations. IEEE Trans Med Imaging 26:1017-34
Chang, Lin-Ching; Koay, Cheng Guan; Pierpaoli, Carlo et al. (2007) Variance of estimated DTI-derived parameters via first-order perturbation methods. Magn Reson Med 57:141-9
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