The data set consists of six transformed diffusion weighted images (DWI) showing a representative axial slice of the human brain. The stored values can directly be passed to estimate the diffusion tensor elements (regression coefficients) using a transform of the applied gradients as regressors.
The first two dimensions provide the transformed signal intensities of one brain slice sized 90 x 75 voxels. The third dimension encodes for the direction of the six applied diffusion weighting gradients.
The present DTI data set was acquired at 1.5 T (Signa Echospeed; GE Medical Systems) using a spin-echo echo-planar sequence with TR/TE = 4200ms/120ms and diffusion gradients in a six noncollinear directions with a b-value of 880 s/mm^2. One axial slice was selected from a volume of six DWI (b = 880 s/mm^2) and one reference image (b = 0 s/mm^2). In-plane resolution amounts to 1.875 x 1.875 mm^2.
The transformation of the raw signal intensities,
y = - 1/b log(S_i/S_0)
is derived from the Stejskal-Tanner equation and is proposed by Papadakis et al.
Diffusion Tensor Imaging was performed at the Max-Planck-Institute of Psychiatry, Munich, Germany.
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Papadakis N. G., Xing D., Huang C. L.-H., Hall L. and Carpenter T. A. (1999). A comparative study of acquisition schemes for diffusion tensor imaging using MRI. Journal of Magnetic Resonance 137, 67-82.
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