Can someone help me to find the define marker?

AFNI Message Board
1

Hello,

I’m using the new AFNI and want to transform the anat image from native space to TAL. I saw a video on Youtube using a define marker menu to do the transformation. But I can’t find it in the new version of AFNI. Can someone help me with that?

Many thanks,
Sarah

2

Most will generally use the @auto_tlrc or auto_warp.py to transform a dataset into a standard space instead of the stereotaxic procedure. Still that procedure is available by right-clicking on the DataDir button (second column of the afni GUI interface).

3

Thank you so much Daniel.
I tried to use @auto_tlrc.py. But I aways got the error info like:

What kind of dataset is this?
– Error 3dSkullStrip (SUMA_3dSkullStrip.c:2019):
Failed to load/prep volume
Error: Failed to create skull stripped brain

Here is the code I’m using:
@auto_tlrc -base TT_N27+tlrc
-suffix SphereM
-input AllSubrg_SphereMask+orig
-init_xform AUTO_CENTER

I’m trying to transform the spheremask including only the small subcortical regions.

4

Not sure what your data looks like. Try running @Align_Centers separately.

5

That program is appropriate for registering a T1 weighted
dataset to the template (typically). It will not work for aligning
a mask.

To align a mask, first run @auto_tlrc on the T1 that the mask
is registered with (see “Usage 1” in the help), and then you can
use @auto_tlrc to apply that same transformation to the mask
(see “Usage 2” in the help).

Does that seem reasonable?

  • rick
6

Thanks Rick~
Yes, that method sounds reasonable, I’ve got the mask in tlrc space.
But I met another problem, when I tried to do the non-linear 3D warp using auto_warp.py, for warping the T1 image to TT_27 template.
Here is the code I used :
auto_warp.py -base TT_N27+tlrc -input ${subject}_TPL.nii
-skull_strip_input no
-affine_input_xmat AUTO -qw_opts -emask
SphereMaskInvers+tlrc
-minpatch 7
Btw, the emask is just a mask of the whole subcortical region.

Here is the error I got:
Warping used up maximum iterations
even after doubling number of iterations to 274.
Convergence might not have been reached.

If you find the alignment poor, try
rerunning the script with a transform
type of fewer parameters (-xform) or by using
an even higher number of iterations (-maxite).
You can also choose to continue with the
current result (dset: __ats_tmp__reg_warpdriveout+orig
by using the option: -OK_maxite

Script will stop …

#**ERROR Failed

Many thanks,
Sarah

7

auto_warp.py and @auto_tlrc both align datasets to similar modality templates. That means your TPL dataset should look something like the template, e.g. T1 to T1 template. Your error shows the processing stopped while doing the affine alignment step. You can run into problems if the datasets start at a coordinate far away from the templates coordinates. @auto_tlrc and auto_warp.py both have options for dealing with that, but I have found it is often simpler to just start off with centers aligned with @Align_Centers. This doesn’t introduce any interpolation and makes the alignment simpler. For the nonlinear alignment, you have specified an exclusion mask of your sphere mask, meaning those won’t be counted in the alignment.