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Subject:

Re: A question about Realignment

From:

John Ashburner <[log in to unmask]>

Reply-To:

John Ashburner <[log in to unmask]>

Date:

Tue, 10 Oct 2000 10:14:56 +0100 (BST)

Content-Type:

TEXT/plain

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TEXT/plain (114 lines)

The first part  of  spatially  transforming  images  via  spatial
normalisation  is  an  affine transform.  These affine transforms
can easily  be  combined  with  the  rigid  body  transformations
estimated at the realignment stage.  This is in fact what is done
in SPM96 and SPM99.

Realignment  only  does  rigid  body   registration,   which   is
parameterised by 3 rotations and 3 translations.  For a number of
reasons, this does not remove all variance in the data  that  can
be  explained by movement.  The main sources of residual variance
that I can think of are:

Interpolation  error  from  the  resampling  algorithm  used   to
transform  the  images  can  be one of the main sources of motion
related artifacts.  When the image series  is  resampled,  it  is
important  to  use  a  very accurate interpolation method such as
sinc or Fourier interpolation.

When MR images are reconstructed, the final  images  are  usually
the  modulus  of  the  initially  complex  data, resulting in any
voxels that should be negative being rendered positive.  This has
implications  when  the images are resampled, because it leads to
errors at the edge of the brain that can not be corrected however
good  the  interpolation  method is.  Possible ways to circumvent
this problem are to work with complex data, or possibly to  apply
a low pass filter to the complex data before taking the modulus.

The sensitivity (slice selection)  profile  of  each  slice  also
plays a role in introducing artifacts.

fMRI images are spatially distorted, and the amount of distortion
depends partly upon the position of the subject's head within the
magnetic field.  Relatively large subject movements result in the
brain  images  changing shape, and these shape changes can not be
corrected by a rigid body transformation.

Each fMRI volume of a series is currently acquired a plane  at  a
time  over  a  period of a few seconds.  Subject movement between
acquiring the first and last plane of any volume leads to another
reason  why  the  images may not strictly obey the rules of rigid
body motion.

After a slice is magnetised, the excited  tissue  takes  time  to
recover  to  its  original state, and the amount of recovery that
has taken place will influence the intensity of the tissue in the
image.  Out of plane movement will result in a slightly different
part of the brain being excited during each repeat.   This  means
that  the  spin  excitation will vary in a way that is related to
head motion, and so leads to more movement related artifacts.

Ghost artifacts in the images do not obey  the  same  rigid  body
rules as the head, so a rigid rotation to align the head will not
mean that the ghosts are aligned.

The accuracy of the estimated registration parameters is normally
in  the  region  of tens of micro-m.  This is dependent upon many
factors, including the effects just mentioned.  Even  the  signal
changes  elicited  by  the experiment can have a slight effect on
the estimated parameters.


These problems can not be corrected by simple image  realignment,
and  so  may  be  sources  of possible stimulus correlated motion
artifacts.  Systematic movement artifacts resulting in  a  signal
change  of only one or two percent can lead to highly significant
false positives over an  experiment  with  many  scans.  This  is
especially  important  for  experiments where some conditions may
cause slight head movements (such as  motor  tasks,  or  speech),
because  these  movements are likely to be highly correlated with
the experimental design. In cases like this, it is  difficult  to
separate   true   activations  from  stimulus  correlated  motion
artifacts. Providing there are enough images in  the  series  and
the  movements  are small, some of these artifacts can be removed
by using an ANCOVA model to remove any signal that is  correlated
with  functions  of  the  movement parameters.  However, when the
estimates of the movement  parameters  are  related  to  the  the
experimental  design,  it  is  likely  that much of the true fMRI
signal will also be lost.  These are still unresolved problems.

Best regards,
-John

| 	When analyzing fMRI data, I normally realign the functional 
| images using the 'coregister only' option.  According to my 
| understanding, this procedure writes a 6-parameter affine transformation 
| into the .mat file associated with each functional image.  The 
| affine transformation describes how to translate and rotate any given 
| functional image such that it is realigned with the reference image.
|  
| 	After realignment, I usually normalize the functional images to MNI 
| space.  From my reading of the SPM documentation, it seems to be the case 
| that the spatial normalization module looks at the .mat file associated 
| with each functional image.  Further, the online help states that it is  
| possible to normalize the functional images without having resliced them 
| first.  When normalizing functional images that have been realigned with 
| the 'coregister only' option, does the normalization module use the .mat 
| file associated with each functional image to create normalized images that 
| are realigned with one another, in addition to being warped to MRI space?
| 
| 	The reason I ask is that we recently found that including motion 
| parameters as regressors during model estimation gets rid of a lot of 
| spurious-looking activations (e.g., activations around the edge of the 
| brain).  This is certainly a good result.  But, I'm wondering why 
| entering motion parameters (produced for each session during realignment) 
| as regressors during model estimation should be so helpful if, in fact, the 
| functional images were already realigned with each other during 
| normalization.  That is, if the motion has already been corrected, how 
| could the motion parameters account for much variance during model 
| estimation?  Any advice would be greatly appreciated!



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