You would use applywarp or flirt to
transform the data according to the structural to standard transformation
matrix.
Peace,
Matt.
From:
Sent: Monday, June 25, 2012 5:25
PM
To: [log in to unmask]
Subject: Re: [FSL] Averaging
tracks across participants
One more question, actually; if my seed were in structural space, and
the output file was thus in structural space, how would I convert the output
file into standard space?
Thanks,
On Mon, Jun 25, 2012 at 4:23 PM, Crystal Zhou <[log in to unmask]>
wrote:
Thanks!
On Mon, Jun 25, 2012 at 4:18 PM, Matt Glasser <[log in to unmask]> wrote:
The output should already be in standard space then.
Peace,
Matt.
From:
Sent: Monday, June 25, 2012 5:03
PM
To: [log in to unmask]
Subject: Re: [FSL] Averaging
tracks across participants
I provided
Probtracks with a transformation matrix (standard2diff.mat - this came from the
Registration step). There is only one output file - fdt_paths.nii.gz. I was
under the impression that this file is in diffusion space. Is automatically
changed standard space? If not, is there a way to convert it to standard space?
I have included
the probtrackx.log and the fdt.log below if they're of any help.
Thanks!
probtrackx.log
/usr/local/fsl/bin/probtrackx
--mode=seedmask -x
/Users/jclab/Desktop/Crystal_2012/Summer_2012/Data/Chinese/P1_Chinese/AXIAL_T1_MPRAGE_Series_3/P1_L_insula.nii
-l -c 0.2 -S 2000 --steplength=0.5 -P 5000
--xfm=/Users/jclab/Desktop/Crystal_2012/Summer_2012/Data/Chinese/P1_Chinese/DTI_Series_CorrectedDICOMImport/DTI.bedpostX/xfms/standard2diff.mat
--forcedir --opd -s
/Users/jclab/Desktop/Crystal_2012/Summer_2012/Data/Chinese/P1_Chinese/DTI_Series_CorrectedDICOMImport/DTI.bedpostX/merged
-m
/Users/jclab/Desktop/Crystal_2012/Summer_2012/Data/Chinese/P1_Chinese/DTI_Series_CorrectedDICOMImport/DTI.bedpostX/nodif_brain_mask
--dir=/Users/jclab/Desktop/Crystal_2012/Summer_2012/Data/Chinese/P1_Chinese/DTI_Series_CorrectedDICOMImport/DTI.bedpostX/L_insula
fdt.log
set tool
probtrackx
set
probtrack(usereference_yn) 1
set probtrack(xfm)
/Users/jclab/Desktop/Crystal_2012/Summer_2012/Data/Chinese/P1_Chinese/DTI_Series_CorrectedDICOMImport/DTI.bedpostX/xfms/standard2diff.mat
set
probtrack(bedpost_dir)
/Users/jclab/Desktop/Crystal_2012/Summer_2012/Data/Chinese/P1_Chinese/DTI_Series_CorrectedDICOMImport/DTI.bedpostX
set probtrack(xfm)
/Users/jclab/Desktop/Crystal_2012/Summer_2012/Data/Chinese/P1_Chinese/DTI_Series_CorrectedDICOMImport/DTI.bedpostX/xfms/standard2diff.mat
set
probtrack(mode) seedmask
set
probtrack(exclude_yn) 0
set
probtrack(usereference_yn) 1
set
probtrack(verbose_yn) 0
set
probtrack(loopcheck_yn) 1
set
probtrack(modeuler_yn) 0
set
probtrack(curvature) 0.2
set
probtrack(nsteps) 2000
set
probtrack(steplength) 0.5
set
probtrack(nparticles) 5000
set
probtrack(reference)
/Users/jclab/Desktop/Crystal_2012/Summer_2012/Data/Chinese/P1_Chinese/AXIAL_T1_MPRAGE_Series_3/P1_L_insula.nii
set
probtrack(output)
/Users/jclab/Desktop/Crystal_2012/Summer_2012/Data/Chinese/P1_Chinese/DTI_Series_CorrectedDICOMImport/DTI.bedpostX/L_insula
On Mon, Jun 25,
2012 at 10:32 AM, Matt Glasser <[log in to unmask]> wrote:
That isn’t the right procedure for what you want, it is for
something else I think. You should provide probtrackx a transformation
matrix or warpfield from standard space to diffusion space for each subject.
This will cause it to output the results from your standard space seed in
standard space as well. You could then just average the tracking results
with fslmaths, or do something more complicated (like divide by the waytotal,
threshold at some level, and binarize on each subject before averaging).
Peace,
Matt.
From:
Sent: Monday, June 25, 2012 11:05
AM
To: [log in to unmask]
Subject: [FSL] Averaging tracks
across participants
Hello there,
I was wondering if
anyone had any ideas about how to average ProbtrackX results across
participants? Basically, what I'm interested in is if there's a way to average
the tracks found using one seed in MNI space for all participants. I found this
thread below, but it seems to stop at one participant, and (barring a general
idea) I'm also not entirely sure what all the steps does and what it is each
step is producing.
Any help would be
greatly appreciated!
On
10/19/09 2:49 PM, "Matt Glasser" <[log in to unmask]" target="_blank">[log in to unmask]>
wrote:
This thread is getting difficult to follow without
all of the previous messages below. Here is the procedure you should be
using:
Create Transforms:
Create affine transform between FA and Template FA using FLIRT (lets call this
diff2standard.mat), also output the linearly transformed FA map in the template
space (lets call this data_FA2standard_affine.nii.gz)
Create nonlinear transform between data_FA2standard_affine.nii.gz and the
template FA (lets call this diff2standardwarp.nii.gz)
Invert Transforms:
Use convert_xfm -omat standard2diff.mat -inverse diff2standard.mat
Use invwarp -w diff2standardwarp.nii.gz -o standard2diffwarp.nii.gz -r
<native diffusion space file, e.g. nodif_brain_mask>
Move ROIs from Standard Space to Structural Space:
applywarp -i <ROI in standard space> -r <native diffusion space file,
e.g. data_FA> -o <ROI in native space> -w standard2diffwarp.nii.gz
--postmat=standard2diff.mat --interp=nn
To test, you can do the following test to verify that you did everything
correctly:
applywarp -i <data_FA in diffusion space> -r <template FA> -o
<data_FA in standard space> --premat=diff2standard.mat -w
diff2standardwarp.nii.gz --interp=trilinear
applywarp -i < data_FA in standard space > -r <native diffusion space
file, e.g. data_FA> -o <data_FA in diffusion space 2> -w standard2diffwarp.nii.gz
--postmat=standard2diff.mat --interp=trilinear
The output image <data_FA in diffusion space 2> should line up exactly
with the original image <data_FA in diffusion space> as you will have
transformed the FA into standard space and then back again into diffusion
space. Note that I use trilinear interpolation instead of nearest
neighbour because the FA is an image with continuous values.
Peace,
Matt.