# 3dTORTOISEtoHere¶

```
Convert standard TORTOISE DTs (diagonal-first format) to standard
AFNI (lower triangular, row-wise) format. NB: Starting from
TORTOISE v2.0.1, there is an 'AFNI output' format as well, which
would not need to be converted.
Part of FATCAT (Taylor & Saad, 2013) in AFNI.
*** NB: this program is likely no longer necessary if using 'AFNI
*** export' from TORTOISE!
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
+ COMMAND: 3dTORTOISEtoHere -dt_tort DTFILE {-scale_fac X } \
{-flip_x | -flip_y | -flip_z} -prefix PREFIX
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
+ OUTPUT:
1) An AFNI-style DT file with the following ordering of the 6 bricks:
Dxx,Dxy,Dyy,Dxz,Dyz,Dzz.
In case it is useful, one can apply 'flips' to the eventual (or
underlying, depending how you look at it) eigenvector directions,
as well as rescale the associated eigenvalues.
+ RUNNING:
-dt_tort DTFILE :diffusion tensor file, which should have six bricks
of DT components ordered in the TORTOISE manner, i.e.,
diagonals first:
Dxx,Dyy,Dzz,Dxy,Dxz,Dyz.
-prefix PREFIX :output file name prefix. Will have N+1 bricks when
GRADFILE has N rows of gradients.
-flip_x :change sign of first element of (inner) eigenvectors.
-flip_y :change sign of second element of (inner) eigenvectors.
-flip_z :change sign of third element of (inner) eigenvectors.
-> Only a single flip would ever be necessary; the combination
of any two flips is mathematically equivalent to the sole
application of the remaining one.
Normally, it is the *gradients* that are flipped, not the
DT, but if, for example, necessary files are missing, then
one can apply the requisite changes here.
-scale_fac X :optional switch to rescale the DT elements, dividing
by a number X>0.
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
+ EXAMPLE:
3dTORTOISEtoHere \
-dt_tort DTI/DT_DT+orig \
-scale_fac 1000 \
-prefix AFNI_DT
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
If you use this program, please reference the introductory/description
paper for the FATCAT toolbox:
Taylor PA, Saad ZS (2013). FATCAT: (An Efficient) Functional
And Tractographic Connectivity Analysis Toolbox. Brain
Connectivity 3(5):523-535.
```