Although the "MoCo" series work fine some cases, we still prefer doing
by ourselves, because we have full control on the procedure, we can
check the realignment time course for abrupt motion, also the
realignment parameters can be used as covariates in GLM. For separate
realignment, you can try the following code. If there is heavy motion
between the first label/control pair, a coregistration and reslice step
should be included later.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%
clear P;
spm_defaults;
% Get realignment defaults
defs = defaults.realign;
% dirnames,
% get the subdirectories in the main directory
fg=spm_figure('FindWin','Graphics');
if isempty(fg)
spm_figure('Create','Graphics','Graphics','on')
else
set(fg,'Visible','on');
spm_figure('Clear',fg);
end
P=spm_get(inf, '*img');
for i=1:2
PP=P(i:2:end,:);
% Flags to pass to routine to calculate realignment parameters
% (spm_realign)
%as (possibly) seen at spm_realign_ui,
% -fwhm = 5 for fMRI
% -rtm = 0 for fMRI
realignfig=sprintf('realignfig%g.jpg',i);
defs.printstr=['print -djpeg -painters -append -noui ' realignfig];
reaFlags = struct(...
'quality', defs.estimate.quality,... % estimation quality
'fwhm', 5,... % smooth before
calculation
'rtm', 0,... % whether to realign to
mean
'PW',''... %
);
% Flags to pass to routine to create resliced images
% (spm_reslice)
resFlags = struct(...
'interp', 1,... % trilinear interpolation
'wrap', defs.write.wrap,... % wrapping info
(ignore...)
'mask', defs.write.mask,... % masking (see
spm_reslice)
'which',2,... % write reslice time
series for later use
'mean',1); % do write mean image
% Run the realignment
spm_realign(PP, reaFlags);
eval(defs.printstr);
% Run the reslicing
spm_reslice(PP, resFlags);
end
-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
On Behalf Of khalid
Sent: Friday, July 15, 2005 1:50 AM
To: [log in to unmask]
Subject: Re: [SPM] Perfusion MRI processing
Dear Darren and others
Do any of you have experience with the Siemens Moco images for ASL, as
these
are already 'motion corrected' would you recommend an additional image
realignment?
Also, I would be very grateful if you could send your matlab function
for
tag to tag and control to control realignment that you mention.
Regards
Khalid
[log in to unmask]; [log in to unmask]
Tel: +44 (0) 1494 601 300 Mobile: +44 (0) 7973 110 337
Fax: +44 (0) 1494 875 666
Clinical Research Fellow,Department of Clinical and Experimental
Epilepsy,
Institute of Neurology, University College London,London, SL9 0RJ, UK
-----Original Message-----
From: Darren Gitelman [mailto:[log in to unmask]]
Sent: 13 July 2005 15:56
Subject: Re: Perfusion MRI processing
my quick answer as I dash to rounds is that i think there are
differences.
I can send a matlab function that realigns tags to tags and controls to
controls later today.
ciao.
darren
At 09:50 AM 7/13/2005, Geoffrey K Aguirre wrote:
>On Jul 13, 2005, at 1:03 PM, Ze Wang wrote:
>
>> For ASL images, I always realign them before subtraction, and
>>for realignment, I just follow the general procedure, although
>>Geoffrey
>>Aguirre recommends the separate realignment.
>
> Because the label and control images have systematically
>different image intensities, it seems wise to realign them back to
>separate label and control initial images. I have, however, never
>tested to see if this makes any substantive difference.
>
>>According to
>>JJ Wang's paper, spatial smoothing is very important for ASL images.
>
> The concept here is that whereas spatial smoothing of BOLD data
>is associated with some deleterious effects upon the intrinsic
>temporal noise structure, this unwanted effect is absent for ASL
>data. In other words, spatial smoothing of ASL data provides the full
>benefit that would be expected. There is no necessary incentive to
>smooth ASL data, however, beyond what would be desired to maximize
>detection of spatially extended signal changes.
>
> For those interested in the technical details: BOLD fMRI data
>have enhanced noise at low temporal frequencies. These low-frequency
>temporal fluctuations tend to share phase across space, so spatial
>smoothing of BOLD data acts to enhance the noise present at low
>temporal frequencies.
>
>Geoff
>
>--
>
>Geoffrey Karl Aguirre, M.D., Ph.D.
>[log in to unmask]
>Assistant Professor of Neurology
>Hospital of the University of Pennsylvania
>Center for Cognitive Neuroscience
>Philadelphia, PA
------------------------------------------------------------------------
-
Darren R. Gitelman, M.D.
Cognitive Neurology and Alzheimer¹s Disease Center
Northwestern Univ., 320 E. Superior St., Searle 11-470, Chicago, IL
60611
Voice: (312) 908-9023 Fax: (312) 908-8789
------------------------------------------------------------------------
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