Dear Christian and Marko (and others that I have thanked already),
Thank you very much for the reply. These images were collected at a 1.5T.
So far I tested some poor images using 1. custom templates created in SPM2
and using a 5-10 yo CCHMC template, 2. reorienting to AC, and many images
look great now.
Choosing 'No Affine Registration' in Affine Regularisation helped in many
tough cases. Can anyone tell me why this may be the case? I did not quite
understand (or find that information) in the manual.
I am now trying Chritian's vbm5 beta version but I am running into some
problems.
I get the following error right away. Do you know what the problem is?
----------
Warning: Input should be a string.
> In spm_maff>priors at 314
In spm_maff>affreg at 115
In spm_maff at 45
In cg_vbm at 182
In cg_config_vbm>execute_estwrite at 723
In spm_jobman>run_struct1 at 1384
In spm_jobman>run_struct1 at 1392
In spm_jobman>run_struct1 at 1392
In spm_jobman>run_struct1 at 1392
In spm_jobman>run_struct at 1351
??? Error using ==> spm_maff>priors
"ƪ" not recognised as type of regularisation.
??? Error while evaluating uicontrol Callback.
-----------
Thanks very much for all your help.
Best,
Fumiko
----- Original Message -----
From: "Christian Gaser" <[log in to unmask]>
To: <[log in to unmask]>
Sent: Wednesday, April 25, 2007 12:32 AM
Subject: Re: [SPM] VBM5 problem in young children
Dear Fumiko,
On Wed, 25 Apr 2007 08:45:17 +0200, Marko Wilke
<[log in to unmask]>
wrote:
>Dear All,
>
>> I知 not convinced that this outcome is primarily due to the mismatch
>> between the characteristics of your study population and the template
>> (although it might be aggravated by it):
>
>I fully agree. Your imanges look like they were not normalized correctly
>(remember that even for native-space segmentation, the priors are in
>efect inversely normalized to the input images). Your results look like
>they are mainly the result of the prior probability maps. This can
>happen when matching the priors goes completely wrong which again can
>happen when you have very inhomogenous input data. This is not
>high-field data by any chance?
>
>> Is it possible that you didn稚 set the origin before preprocessing the
>> data? In our experience the unified segmentation approach is pretty
>> vulnerable to end up with such deformed results if you don稚 give the
>> program a reasonable starting point. Assuming that the data quality is
>> o.k., my best guess would be: reset the origin to AC, rerun data
>> preprocessing for these subjects, and you池e done.
>
>This may help, too. Christian had implemented an automated determination
>of the center of mass of an image in order to improve the starting
>estimates in a beta-version of the 5.1 toolbox but I don't know if it is
>out yet.
I also assume that spatial registration was not correct. You may try the new
VBM5.1 Toolbox,
which is still a beta version with many new (and not yet documented)
features (e.g. correction for
non-isotropic smoothness, segmentation without priors, pre-registration
using center of mass):
http://dbm.neuro.uni-jena.de/vbm/download/
>
>Also,
>
>> The ages of the subjects are 1-3 years old.
>
>there is no reference data for such subjects yet that I am aware of. We
>(Christian, the Cincinnati IRC group and myself) have two abstracts at
>HBM where we investigate a prior-less segmentation for datasets from
>infants. In effect, the segmentation is done as in spm5 but for writing
>out the results only tissue intensity information is used, disregarding
>the influence from the priors. It does make the segmentation somewhat
>more vulnerable, especially w.r.t. inhomogeneity, but it seems to
>considerably improve segmentation results on "unusual" datasets. Not
>sure if the updated 5.1 toolbox is already publicly available but you
>could ask Christian.
Thanks for the push Marko! As he already mentioned, it might be helpful for
children/infant data
to try the new prior-free segmentation approach, which can be found in the
extended options.
Best,
Christian
--
____________________________________________________________________________
Christian Gaser, Ph.D.
Assistant Professor of Computational Neuroscience
Department of Psychiatry
Friedrich-Schiller-University of Jena
Philosophenweg 3, D-07743 Jena, Germany
Tel: ++49-3641-935805 Fax: ++49-3641-935280
e-mail: [log in to unmask]
http://dbm.neuro.uni-jena.de
>
>> The segmented images come out like this (please see attached jpg of
>> brains with modulation, segmentation and normalization applied; the left
>> brain is the one with the problem, the right brain is for comparison).
>
>Ah, good to know ;) But again, this is a problem with matching the
>priors and thus effectively a normalization problem. Note the oblique
>cutoff at the upper left side, this is where either the FOV or the
>tissue boundary of the input image went. Try the suggestions above and
>let us know how you fared.
>
>Best,
>Marko
>--
>===========================================================
==========
>Marko Wilke (Dr.med./M.D.)
> [log in to unmask]
>
>Universit舩s-Kinderklinik University Children's Hospital
>Abt. III (Neurop臈iatrie) Dept. III (Pediatric neurology)
> Hoppe-Seyler-Str. 1, D - 72076 T・ingen
>Tel.: (+49) 07071 29-83416 Fax: (+49) 07071 29-5473
>===========================================================
==========
>===========================================================
=============
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