7.1.66. 3dDWItoDT

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Usage: 3dDWItoDT [options] gradient-file dataset Computes 6 principle direction tensors from multiple gradient vectors

and corresponding DTI image volumes. The program takes two 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). a 3D bucket dataset with Np+1 sub-briks where the first sub-brik is the volume acquired with no diffusion weighting.

Options:

-prefix pname = Use ‘pname’ for the output dataset prefix name.
[default=’DT’]
-automask = mask dataset so that the tensors are computed only for
high-intensity (presumably brain) voxels. The intensity level is determined the same way that 3dClipLevel works.
-mask dset = use dset as mask to include/exclude voxels
-bmatrix_NZ = input dataset is b-matrix, not gradient directions, and
there is no row of zeros at the top of the file, similar to the format for the grad input. There must be 6 columns of data, representing either elements of G_{ij} = g_i*g_j (i.e., dyad of gradients, without b-value included) or of the DW scaled version, B_{ij} = b*g_i*g_j. The order of components is: G_xx G_yy G_zz G_xy G_xz G_yz.
-bmatrix_Z = similar to ‘-bmatrix_NZ’ above, but assumes that first row of the
file is all zeros, i.e. the bmatrix includes a B=0 volume as the first volume. Note that the first row is ignored, so that if you do have a non-zero gradient as the first volume, then that would be ignored and treated as a B=0, no gradient volume
*****NOTE: The former bmatrix option is no longer available!!!!
That option produced an error or incorrect results
-nonlinear = compute iterative solution to avoid negative eigenvalues.
This is the default method.
-linear = compute simple linear solution.
-reweight = recompute weight factors at end of iterations and restart
-max_iter n = maximum number of iterations for convergence (Default=10).
Values can range from -1 to any positive integer less than 101. A value of -1 is equivalent to the linear solution. A value of 0 results in only the initial estimate of the diffusion tensor solution adjusted to avoid negative eigenvalues.
-max_iter_rw n = max number of iterations after reweighting (Default=5)
values can range from 1 to any positive integer less than 101.
-eigs = compute eigenvalues, eigenvectors, fractional anisotropy and mean
diffusivity in sub-briks 6-19. Computed as in 3dDTeig
-debug_briks = add sub-briks with Ed (error functional), Ed0 (orig. error),
number of steps to convergence and I0 (modeled B0 volume)
-cumulative_wts = show overall weight factors for each gradient level
May be useful as a quality control
-verbose nnnnn = print convergence steps every nnnnn voxels that survive to
convergence loops (can be quite lengthy).
-drive_afni nnnnn = show convergence graphs every nnnnn voxels that survive
to convergence loops. AFNI must have NIML communications on (afni -niml)
-sep_dsets = save tensor, eigenvalues,vectors,FA,MD in separate datasets
-csf_val n.nnn = assign diffusivity value to DWI data where the mean values
for B=0 volumes is less than the mean of the remaining volumes at each voxel. The default value is 3.0. The assumption is that there are flow artifacts in CSF and blood vessels that give rise to lower B=0 voxels.
-csf_fa n.nnn = assign a specific FA value to those voxels described above
The default is 0.012345678 for use in tractography programs that may make special use of these voxels
-opt mname = if mname is ‘powell’, use Powell’s 2004 method for optimization
If mname is ‘gradient’ use gradient descent method. If mname is ‘hybrid’, use combination of methods. MJD Powell, “The NEWUOA software for unconstrained optimization without derivatives”, Technical report DAMTP 2004/NA08, Cambridge University Numerical Analysis Group – http://www.damtp.cam.ac.uk/user/na/reports.html
-mean_b0 = use mean of all b=0 volumes for linear computation and initial linear
for nonlinear method
Example:
3dDWItoDT -prefix rw01 -automask -reweight -max_iter 10
-max_iter_rw 10 tensor25.1D grad02+orig.

The output is a 6 sub-brick bucket dataset containing Dxx,Dxy,Dyy,Dxz,Dyz,Dzz (the lower triangular, row-wise elements of the tensor in symmetric matrix form) Additional sub-briks may be appended with the -eigs and -debug_briks options. These results are appropriate as the input to the 3dDTeig program.

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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