Home Computer Science Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016
Data Acquisition and Preprocessing
Baseline MRI, dMRI, and clinical data were downloaded from the ADNI database (adni.loni.usc.edu). Here we performed an analysis of dMRI data from 102 participants: 53 healthy controls (CN; mean age: 72.4 ± 6.0 years; 24 M/29 F), and 49 AD patients (mean age: 74.9 ± 8.7 years; 29 M/20 F).
All subjects underwent whole-brain MRI scanning on 3T GE Medical Systems scanners at 14 acquisition sites across North America. Anatomical T1- weighted SPGR (spoiled gradient echo) sequences (256 x 256 matrix; voxel size = 1.2 x 1.0 x 1.0 mm3; TI = 400 ms; TR = 6.98 ms; TE = 2.85 ms; flip angle = 11°), and dMRI (128 x 128 matrix; voxel size: 2.7 x 2.7 x 2.7 mm3; TR = 9000 ms; scan time = 9 min were acquired; 46 separate images were acquired for each dMRI scan: 5 images with no diffusion sensitization (b0 images) and 41 diffusion-weighted images (DWI; b = 1000 s/mm2).
Images were preprocessed as in . To summarize, raw dMRI images were corrected for motion and eddy current distortions, and T1-weighted images underwent inhomogeneity normalization. Extra-cerebral tissue was removed from both scan types. Each T1-weighted anatomical image was linearly aligned to a standard brain template (the down-sampled Colin27 ): 110 x 110 x 110, with 2-mm isotropic voxels). The diffusion images were linearly and then elastically registered  to their respective T1-weighted structural scans to correct for echo-planar imaging induced susceptibility artifacts. The gradient tables were corrected to account for the linear registration of the DWI images to the structural T1-weighted scan.
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