7.1.64. 3dDTtoDWI

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Usage: 3dDTtoDWI [options] gradient-file I0-dataset DT-dataset Computes multiple gradient images from 6 principle direction tensors and

corresponding gradient vector coordinates applied to the I0-dataset.
The program takes three parameters as input :
a 1D file of the gradient vectors with lines of ASCII floats Gxi,Gyi,Gzi. Only the non-zero gradient vectors are included in this file (no G0 line).

The I0 dataset is a volume without any gradient applied. The DT dataset is the 6-sub-brick dataset containing the diffusion tensor data,

Dxx, Dxy, Dyy, Dxz, Dyz, Dzz (lower triangular row-wise order)

Options:

-prefix pname = Use ‘pname’ for the output dataset prefix name.
[default=’DWI’]
-automask = mask dataset so that the gradient images are computed only for
high-intensity (presumably brain) voxels. The intensity level is determined the same way that 3dClipLevel works.
-datum type = output dataset type [float/short/byte] (default is float).
-help = show this help screen.
Example:
3dDTtoDWI -prefix DWI -automask tensor25.1D ‘DT+orig[26]’ DT+orig.
The output is a n sub-brick bucket dataset containing computed DWI images.
where n is the number of vectors in the gradient file + 1

INPUT DATASET NAMES

This program accepts datasets that are modified on input according to the following schemes:

‘r1+orig[3..5]’ {sub-brick selector} ‘r1+orig<100..200>’ {sub-range selector} ‘r1+orig[3..5]<100..200>’ {both selectors} ‘3dcalc( -a r1+orig -b r2+orig -expr 0.5*(a+b) )’ {calculation}

For the gruesome details, see the output of ‘afni -help’.

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