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Home arrow Computer Science arrow Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016

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Methods

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 [7]. 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 [11]): 110 x 110 x 110, with 2-mm isotropic voxels). The diffusion images were linearly and then elastically registered [12] 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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