Hi Mark,
Thanks for the feedback. I think this all makes sense, but I do have have a couple of follow up questions.
1) Could I re-run prestats, turn off the mcflirt option, and use the transforms estimated previously (e.g., prestats_with_mc.feat/mc/prefiltered_func_data_mcf.mat/MAT_0000)? I still want to temporally filter and brain extract the data. However, I suspect brain extraction might be suboptimal if done before motion correction, but it seems like the registration might also be suboptimal if non-brain material is left in the image.
2) We've created a study-specific EPI template (using ANTs; see http://www.duke.edu/~dvs3/MNIdiffeo.nii.gz). We were hopeful this would serve as a good reference image for registration -- but it is a brain-only image, which isn't ideal for FNIRT according to the documentation. Alternatively, we have a similar study-specific template created from our T1 scans (brain extracted and whole head). Which template would be better, especially if one of our goals is to interpolate the functional data as little as possible?
Sorry for all the questions. I want to make sure I'm getting the most out of my data and the tools that are analyzing it. If I'm getting too specific, I'd be happy to continue this discussion off the list.
Thanks again.
Cheers,
David
On Jun 14, 2011, at 4:19 PM, Mark Jenkinson wrote:
> Hi David,
>
> If you want your data in standard space then FNIRT is
> definitely the way to go. So you could follow the
> same procedure but split the original (pre-motion-correction)
> data into individual volumes and then use the final applywarp
> call, but you'll need to use a different --premat matrix.
> It needs to be a combination of the appropriate
> mcflirt transformation matrix with the
> example_func2highres.mat using convert_xfm (with
> the -concat option). I hope that makes sense.
> Oh, and you can also try the spline interpolation
> option with applywarp.
>
> All the best,
> Mark
>
>
>
>
> On 14 Jun 2011, at 19:56, David V. Smith wrote:
>
>> Hi Mark,
>>
>> Sorry -- I forgot to specify that "data" was my filtered_func_data file from Pre-Stats.
>>
>> OK, I understand what you're saying. We certainly want to reduce interpolation as much as possible -- e.g., no smoothing and I made a standard template that has approximately the same resolution as our functional data (1.8 x 1.8 x 1.8 mm). However, we want the best possible registration, so I imagine FNIRT is the way to go.
>>
>> Thanks,
>> David
>>
>>
>> On Jun 14, 2011, at 2:38 PM, Mark Jenkinson wrote:
>>
>>> Dear David,
>>>
>>> I'm not sure what "data" is in the last call, but what you have is
>>> correct. What is different from the previous question is that you
>>> do not include any motion correction here, as motion correction
>>> has a separate matrix for every timepoint. However, if you
>>> wanted to combine motion correction and warping to standard
>>> space with one transformation (and hence reduce interpolation
>>> effects) then you'd need to do what I suggested in the previous
>>> email.
>>>
>>> If you do not have 4D data or you are doing smoothing on the
>>> data after it is resampled like this, then you don't really need to
>>> worry about anything else and the commands you are running
>>> here will be absolutely fine.
>>>
>>> All the best,
>>> Mark
>>>
>>>
>>> On 14 Jun 2011, at 18:49, David V. Smith wrote:
>>>
>>>> Hi Mark,
>>>>
>>>> I have a follow up question. I used FNIRT to normalize my preprocessed 4D data prior to analysis (code below). My output looks OK, but I didn't use applyxfm4D. Does this not matter since I used FNIRT on data that had already been preprocessed (including motion correction)? Please let me know if you think I did anything wrong.
>>>>
>>>> ${FSLDIR}/bin/fnirt --in=highres --aff=highres2standard.mat --cout=highres2standard_warp --iout=highres2standard --jout=highres2standard_jac --config=T1_2_MNI152_2mm --ref=standard --refmask=standard_mask --warpres=9,9,9 --applyrefmask=0,1,1,1,1,1
>>>> ${FSLDIR}/bin/convert_xfm -inverse -omat standard2highres.mat highres2standard.mat
>>>> ${FSLDIR}/bin/convert_xfm -omat example_func2standard.mat -concat highres2standard.mat example_func2highres.mat
>>>> ${FSLDIR}/bin/applywarp --ref=standard --in=example_func --out=example_func2standard --warp=highres2standard_warp --premat=example_func2highres.mat --interp=sinc
>>>> ${FSLDIR}/bin/convert_xfm -inverse -omat standard2example_func.mat example_func2standard.mat
>>>> ${FSLDIR}/bin/applywarp --ref=standard --in=data --out=data2standard --warp=highres2standard_warp --premat=example_func2highres.mat
>>>>
>>>> (Note that I'm not using FEAT for subsequent analyses, so the conventional approach of normalizing the cope images is not what I need here.)
>>>>
>>>> Thanks,
>>>> David
>>>>
>>>>
>>>> On Jun 14, 2011, at 12:47 PM, Mark Jenkinson wrote:
>>>>
>>>>> Hi,
>>>>>
>>>>> I'm afraid applywarp will only take one matrix at a time.
>>>>> The tool applyxfm4D would take a set of matrix files, but
>>>>> unfortunately does not yet support spline interpolation.
>>>>> We will be putting spline interpolation into these tools in
>>>>> the future though, but at the moment you need to do
>>>>> fslsplit, loop applywarp, then fslmerge.
>>>>>
>>>>> All the best,
>>>>> Mark
>>>>>
>>>>>
>>>>> On 14 Jun 2011, at 17:39, Satrajit Ghosh wrote:
>>>>>
>>>>>> hi
>>>>>>
>>>>>> if i run mcflirt on a 4d time series and generate the list of mat files for the transforms. can i then send the single 4d mat file and the list of transforms to applywarp (in order to use spline interpolation)? or will i need to split the 4d file into 3d files, apply the transforms individually and then merge them back in?
>>>>>>
>>>>>> cheers,
>>>>>>
>>>>>> satra
>>>>>>
>>>>
>>
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