Hi,

The nosearch option is equivalent to searchrx/y/z being set to zero, so
should be very similar.

I would not recommend using bet masks as weights in general as they
can remove the ability of the method to use edge information.  If you
do not include any background voxels in the masks then the method
has no idea that there is any brain/background edge and that can lead
to bad results.  If you want to make it insensitive to the precise
details
of the bet mask then I would dilate the mask a few times to include
the voxels just outside of the brain but not much of the total non-brain
volume.

I guess it depends on what the real distortions are like, but if you
cannot
get good fieldmap data then you are right that it isn't useful to try
and
apply them.  However, doesn't this also affect your EPI data?

Anyway, a 12 DOF approach sounds reasonable, but might be more
robust if you do an initial 6 DOF first and then try the 12 DOF.  You
could also fix the scale in the first stage (to a known non-unity value)
if that helped.

Hope you have a good holiday and let me know how you get on when
you return.

All the best,
	Mark



On 2 Jan 2009, at 12:13, wolf zinke wrote:

> Hi Mark,
>
> Thanks for the reply.
> Mark Jenkinson wrote:
>> Hi,
>>
>> If you are dealing with images that are almost aligned at the
>> beginning then I find
>> it quite worrying that they do not register well.  Are you using
>> the -nosearch option?
>> This turns off the initial (large) search phase, but allows the
>> perturbations and
>> local refinement of the transformation.
> Usually I do not use the -nosearch option, but I set the -searchrx\y
> \z quiet low (~ 3 deg) to account for small differences between
> sessions. I only use the -nosearch option if I alligned the data
> already to a common template.
>>
>> Also, are there significant distortions between the images you are
>> registering?
> The last time when I encounter this problem I was  coregistering a
> magniude image of the fieldmap (low resolution) with a FLASH image
> (high resolution), using a bet masks of both modalities as weight.
> So, here are not much distortions in the data, but maybe due to the
> resolution difference flirt thought that the magnitude bet image
> corresponds just to the cerebellum. I improved the results by not
> using the weight mask. I also reduced DOF to 6 again.
>> It is possible that it cannot find a good affine registration
>> because of this.
>> Are you using more than 7 dof because of these distortions?  If so,
>> a fieldmap
>> approach (unwarping the images using the fieldmap information)
>> would be preferable if you have them.
> In the EPI images there are of course nice distortions since it is
> awake behaving monkey data. I tried epi undistortions a while ago,
> but was not very happy with the result. In general I got
> improvements of the global brain shape, but locally I introduced
> severe artefacts. I interpreted it as a problem resulting from
> dynamically changes in the field due to swallowing (large muscle
> movements) and body movements. As long the fieldmap is not acquired
> close in time to the epi I would expect problems. However, if I got
> some spare time I want to try out a few more things. However, I had
> the impression that the 12 DOF affine transformation gave very good
> results with the coregistrations. Functional maps are usually pretty
> well located in the grey matter and allow for a reliable
> localization. Fortunately the problem I described here does not
> occur very often (< 10 %) and I can cope with it by manually
> adjusting the coregistration parameter.
>
> If you want to try something with the data I could select some files
> and upload them, but I am not sure how fast I could manage it,
> because right now I should have some holidays. Anyway, I'll prepare
> a selection soon.
>
> Thanks,
> wolf
>
>>
>> There is no explicit constraint on the parameters.  If you cannot
>> get a good
>> solution at present this is really unlikely to help as it will most
>> likely just give
>> you a solution where it is against the hard limit on one or more
>> parameters
>> which may not be any better than your original position.  You can
>> explicitly
>> set the scaling by passing in an initial matrix where the scaling
>> is set (via
>> the -init option) and setting the dof to 6 so that it will not
>> change the scaling.
>> However, for the reasons above I do not really recommend this.
>> Certainly
>> I would not recommend trying things with schedule files.
>>
>> If your images are not significantly distorted and are of the same
>> individual
>> and you still cannot get a good registration with 6 (or 7) dof and
>> the -nosearch
>> option (or, better, using fieldmap unwarping) then feel free to
>> send us some
>> example images for us to try via our upload site:
>>   http://www.fmrib.ox.ac.uk/cgi-bin/upload.cgi
>>
>> All the best,
>>    Mark
>>
>>
>> On 26 Dec 2008, at 00:11, wolf zinke wrote:
>>
>>> Hi,
>>>
>>> Here is something for my Xmas wishlist - I noticed that flirt
>>> sometimes gives some weird results when using a 9 or 12 DOF
>>> registration. It manages to put the whole brain for example into
>>> the cerebellum of the reference image. I guess this misbehaviour
>>> would be improved if there are restrictive constraints that limit
>>> the scaling. Another option allowing to specify the scaling
>>> explicitly might be very helpful (as it is realized for the
>>> rotation angles).  It also could help if this is  done for all
>>> other fitting parameter as well, especially for the translation.
>>> Since I am working with primates having their head fixed I do not
>>> expect much real movement besides the artefacts caused by body
>>> displacements. The latter result primarily in an apparent head
>>> displacement in phase encode direction. Therefore I assume that
>>> using explicit constraints for each parameter for flirt and
>>> mcflirt might give even better results. Maybe this finer control
>>> is already available with the schedule files. In this case I am
>>> sorry that I didn't spent much time yet to understand the
>>> structure of these schedule files and to do this would then be one
>>> of my New Year's pledges.
>>>
>>> Thanks a lot for all the great tools,
>>> wolf
>