[FieldTrip] SPM12 for segmentation and (inverse) normalization
Stephen Whitmarsh
stephen.whitmarsh at gmail.com
Fri Sep 22 10:09:11 CEST 2017
Hi Arjen,
Indeed, I do not think there is a problem with segmentation of brain/skull,
as can be seen on the image. Stripping some skin of two subjects
(thresholding the 'soft_tissue' probability output of SPM12, then removing
it) with a similar problem of solved the rotation, but resulted in too
small grids... On this subject I attached this procedure has no effect.
However, at least for those other subjects normalization on skullstripped,
or rather, scalpstripped MRIs might do the trick. As I understand it,
however, the spm8 procedure (and spm12 I think) is a two-stepped procedure,
with (affine) transformation based on the scalp first, after which it
optimizes it based on brain segmentation. I would not know how to therefor
do normalization without scalp. In fact, it expects a full volumetric
image, not a (pre-)segmented one.
Cheers,
Stephen
On 21 September 2017 at 18:45, Arjen Stolk <a.stolk8 at gmail.com> wrote:
> First thought is a registration of brain outline to skull (instead of
> brain), although at closer inspection the shift seems overall just a bit
> too large for that. You could try calculating the normalization parameters
> on skullstripped volumes (unless you want to keep non-brain tissue).
>
> On Thu, Sep 21, 2017 at 9:20 AM, Stephen Whitmarsh <
> stephen.whitmarsh at gmail.com> wrote:
>
>> Hi Arjen,
>>
>> Thanks, and good to hear you've not been let down yet. It might be the
>> fact that I have some bad quality MRIs to deal with. However... does this
>> problem (see attached) ring a bell for anyone?:
>>
>> Brain segmentation is proper, and co-registration with polhemus
>> head-shape as well, but inverse warp to MNI result in a tilted grid.
>> Linear vs. non-linear transformation gives the same result. Other subjects
>> going through the same procedure work fine, except two others wherein I
>> identified it as a problem in segmenting the scalp and therefor the first
>> step of the normalization. This one looks absolutely fine in every other
>> regard, however.
>>
>> I'm stumped...
>>
>> cfg = [];
>> cfg.spmversion = 'spm12';
>> cfg.grid.warpmni = 'yes';
>> cfg.grid.template = template_grid;
>> cfg.grid.nonlinear = 'yes';
>> cfg.mri = mri_realigned;
>> cfg.grid.unit = 'mm';
>> subject_grid = ft_prepare_sourcemodel(cfg);
>>
>> Cheers,
>> Stephen
>>
>>
>> On 21 September 2017 at 17:00, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>>
>>> Hey Stephen,
>>>
>>> Look for discussions regarding spm12 and also dartel on bugzilla. It's
>>> been a while but as far as I can remember ft_volumenormalize is the only
>>> function now that has not been integrated. Reason being that it wasnt
>>> straightforward to house the dartel procedure under a single function, so
>>> this is ongoing work still. You can however use spm12's coregistration
>>> function with ft_volumerealign (for rigid body transformations), which Im
>>> using quite a bit and never let me down (and is much faster than before).
>>> But that wouldnt work for normalization to template space though (use spm8).
>>>
>>> Best
>>>
>>> On Sep 21, 2017, at 6:56 AM, Herbert J Gould (hgould) <
>>> hgould at memphis.edu> wrote:
>>>
>>> I have retired please remove me from the mail list
>>>
>>> Herbert Jay Gould
>>> Professor Emeritus
>>> The University of Memphis
>>>
>>>
>>>
>>> Sent from my Verizon Wireless 4G LTE smartphone
>>>
>>>
>>> -------- Original message --------
>>> From: Stephen Whitmarsh
>>> Date:09/21/2017 7:43 AM (GMT-06:00)
>>> To: FieldTrip discussion list
>>> Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse)
>>> normalization
>>>
>>> Dear Sarang and Jan-Mathijs,
>>>
>>> Thanks a lot. I am now able (after updating FT, which now includes SPM12
>>> in /external), to use SPM12 for segmentation of my template and my subject
>>> MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big
>>> improvement over 8 when it comes to brain-segmentation, which now does not
>>> require individual treatments anymore. It also outputs more compartments
>>> which gives me a little bit more to work with when dealing with scans that
>>> have bad delineation of the scalp for normalization.
>>>
>>> Pleas note that defaults seems to differ - some FT functions default to
>>> spm8, others to spm12.
>>>
>>> In fact, FT still reverts to spm8 in ft_volumenormalise when called in
>>> ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion =
>>> 'spm12'. In other words the cfg.spmversion is not passed along.
>>>
>>> Best wishes and thanks again!
>>> Stephen
>>>
>>>
>>>
>>> On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <
>>> jan.schoffelen at donders.ru.nl> wrote:
>>>
>>>> Hi Stephen,
>>>>
>>>> Please note that FT now has full support for SPM12, both using the
>>>> old-style segmentation, and the new one (the latter yielding 6 tissue
>>>> types).
>>>>
>>>> Best,
>>>> Jan-Mathijs
>>>>
>>>> On 20 Sep 2017, at 17:03, Stephen Whitmarsh <
>>>> stephen.whitmarsh at gmail.com> wrote:
>>>>
>>>> Dear all,
>>>>
>>>> I having some problems in normalizing MRIs for my study. Some have
>>>> improper segmentation for which changing individual brain/scalp thresholds
>>>> works in many cases but not all, e.g. when the scalp 'bleeds' into some
>>>> noise outside of the head. Also, changing parameters in spm8 for
>>>> normalization, such as number of iterations (directly in in spm_normalize,
>>>> since FT does not pass these parameters) improves the transformation.
>>>>
>>>> However, some scans I cannot deal with, either because they have noise
>>>> from outsides of the head 'bleed' onto the scalp, thereby preventing
>>>> optimal scalp-segmentation and thereby normalization. Others have an
>>>> inappropriate contrast MRI sequence.
>>>>
>>>> Some fMRI researchers advised me to use SPM12, because of its improved
>>>> preprocessing procedures. However, it does not seem supported in FT yet.
>>>> Does anyone have experience with this, and can perhaps share how they
>>>> extracted the transformation matrix from the resulting nifti's?
>>>>
>>>> Thanks,
>>>> Stephen
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>>>>
>>>>
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