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SPM  April 2007

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Subject:

Re: VBM5 problem in young children

From:

Paul Macey <[log in to unmask]>

Reply-To:

Paul Macey <[log in to unmask]>

Date:

Wed, 25 Apr 2007 11:10:08 -0700

Content-Type:

text/plain

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Parts/Attachments

text/plain (121 lines)

Hi

Have you tried coregistering the brain to the template first? I have 
found that segmentation fails for images that start a long way from the 
template space - in fact the documentation mentions this, and suggests 
that brains be within ~30 mm of the template as I recall.

Best wishes,
Paul

Fumiko Hoeft wrote:
> 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
>

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