AFNI program: 3dDTtoDWI
Output of -help
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.
-scale_out_1000 = matches with 3dDWItoDT's '-scale_out_1000'
functionality. If the option was used
there, then use it here, too.
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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Tue Dec 17 06:52:36 PM EST 2024