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FSL  August 2009

FSL August 2009

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Subject:

Re: featquery using linear vs. non-linear inverse registration

From:

Jesper Andersson <[log in to unmask]>

Reply-To:

FSL - FMRIB's Software Library <[log in to unmask]>

Date:

Fri, 14 Aug 2009 14:45:10 +0100

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (125 lines)

Dear Stephane,

have you run Featquery_gui to obtain these masks? If so, did you keep  
the settings for

"Do not binarise mask (allow weighting)"
and
"Change post-interpolation thresholding of mask"

constant for the two cases?

When I look at your masks in native space it appears that the "linear"  
mask hasn't been thresholded at all (i.e. it contains a range of  
values 0 < v <= 1) which when all counted amounts to 72 voxels. The  
non-linear mask on the other hand seems to have been thresholded at a  
very high level, containing a single non-zero voxel with the intensity  
1.

If on the other hand I do a non-linear transform of your standard- 
space mask into native space I get a similarish set of voxels to what  
you have in in your linearly transformed mask. If I then threshold  
both at a 0.5 level I end up with 6 non-zero voxels for the linear  
case and 8 voxels for the non-linear case. I.e. a fairly reasonable  
correspondence.

Also, the only remaining voxel in your non-linear mask coincides with  
the voxel with the highest intensity after my non-linear transform  
prior to thresholding.

Hence, is looks as if the difference is due to on the one had no post- 
interpolation thresholding at all (your linear mask) and on the other  
hand very severe post-interpolation thresholding (thr>0.99) for your  
non-linear mask.

Can you please try to run Featquery_gui again, making sure to keep  
these settings constant, and see if you get the same results?

Jesper

On 14 Aug 2009, at 13:23, StéŽphane Jacobs wrote:

> Hi Jesper,
>
> Thanks a lot for your response and your help. The reference number  
> for the upload is 622827.
>
> I have included the 2 versions of the mask in example_func space,  
> obtained with the linear and non-linear registrations.
>
> Best,
>
> Stéphane
>
>
>
> Stéphane Jacobs - Chercheur post-doctorant / Post-doctoral researcher
>
> Espace et Action - Inserm U864
> 16 avenue du Doyen Lépine
> 69676 Bron Cedex, France
> Téléphone / Phone: (+33) (0)4-72-91-34-38
>
>
>
> Jesper Andersson a écrit :
>> Dear Stephane,
>>
>>> I'm using Featquery to compute percent signal change from various  
>>> regions of
>>> interest. I've first run Featquery with the default settings,  
>>> which were to
>>> use the inverse of non-linear registration from highres2standard  
>>> and the
>>> inverse of the affine transformation from example_func2highres.  
>>> However, I
>>> noticed that several subjects (I still need to check the others),  
>>> the
>>> resulting mask transformed from standard to native EPI space is  
>>> excessively
>>> small, 2 or 3 voxels at best, sometimes empty, while the volume of  
>>> the mask
>>> in standard space is 528 mm2 (66 voxels). I checked the
>>> example_func2standard registration and it looks fine.
>>>
>>> To compare, I manually registered the standard mask to EPI space  
>>> using only
>>> the inverse of the affine transformations from  
>>> example_func2highres and
>>> highres2standard, using the default interpolation method and
>>> post-interpolation threshold value. Now, I obtain a 72 voxel mask  
>>> (2812 mm2)
>>> that is even bigger than the original one in standard space...
>>>
>>> At this point I'm a bit confused as to why the mask obtained with  
>>> linear
>>> "back-registration" is so big (might need to adjust the post- 
>>> interpolation
>>> threshold?), but what I really don't get is why the results  
>>> between the 2
>>> registration methods are so different.
>>> Any hint as to what I might have done wrong would be greatly  
>>> appreciated!
>>
>> I must admit to being confused too. I have had a look at the  
>> scripts, and right now I don't have an idea so I would probably  
>> need to have a look at your data.
>>
>> Could you please tar up your
>>
>> example_func image file
>> image file with mask in standard space
>> highres2standard_warp image file
>> highres2standard_warp_inv image file
>> highres2example_func.mat
>> standard2example_func.mat
>> image file with mask after transformation into example_func space
>>
>> and then upload them to http://www.fmrib.ox.ac.uk/cgi-bin/upload.cgi
>>
>> and send me the reference number?
>>
>> Jesper
>>
>

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