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Subject:

Re: Help with DTI DARTEL

From:

Jasmin Tröstl <[log in to unmask]>

Reply-To:

Jasmin Tröstl <[log in to unmask]>

Date:

Fri, 27 May 2011 16:12:38 +0100

Content-Type:

text/plain

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Dear dartel specialists,

I am following the outlines from the paper posted in the previous message: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2889147/?tool=pubmed
Everything works fine until the following steps:

"(c) the custom FA template was normalized to the custom T1-weighted template using 12 parameters in FLIRT [http://www.fmrib.ox.ac.uk/fsl/flirt/index.html, 37];
(d) native space FA and MD images were normalized to the custom FA template (from step (c));
(e) the normalization parameters from step (d) were multiplied by the inverse transformation parameters for the second T1-weighted segmentation (VBM step (iv)) and applied to both the FA and MD images;
(f) FA and MD images were rigidly aligned using the rigid-body component of the transformations produced in VBM step (iv) and resampled to 1.5 mm isotropic voxels;"

but step (d) and (e) make problems.

In step (d) I just normalize the FA and MD images to the new custom FA template using Estimate and Normalize from SPM8.

In step (e) I use the inverse transformation matrix from the second run of the VBM5.1 toolbox estimate and write (segment and normalization to custom prior I calculated from the first run). I just use normalize(write) to normalize the images resulting from step (d).

In step (f) I apply the non inverse transformation matrix from the second run of the VBM5.1 on the results of (e) using Normalise(write) with following options: bounding box filled with NaNs and Voxelsize with 1.5 1.5 1.5

when I look at the results I see the following:

after step (d) the images are kind of "compressed" and after step (e) parts of the brain are just missing (cut off).

I try to follow the guidelines of the paper mentioned above. I am new to VBM-Dartel methods and I don't know what I am doing wrong.
Thank you in advance for every hint and improvement you can give me!

Best regards
Jasmin Tröstl

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