Afni3dDespike

Afni3dDespike is a wrapper for the AFNI (RW Cox, NIH) 3dDespike application.
Removes 'spikes' from the 3D+time input dataset and writes a new dataset with the spike values replaced by something more pleasing.
The AFNI 3dDespike program was written by B. Douglas Ward, Medical College of Wisconsin.
For more information about the AFNI package, see the AFNI Homepage

Currently there is no postscript documentation available for this application.

To read the help documentation generated from the command line go here.

Invocation

java  Afni3dDespike

This starts up the java interpreter and runs the Afni3dDespike application. You need to have set up your environment for java in order for this to work.

Afni3dDespike window

Information to be entered

Input dataset
Restrict files to AFNI 3D+time datasets only. The input dataset may use a sub-brick selection list, as in program 3dcalc.
Ignore
Ignore the first I points in the time series: these values will just be copied to the output dataset [default I=0].
Curve Fit Order
Set the curve fit order to L: the curve that is fit to voxel data v(t) is
```
                       k=L [        (2*PI*k*t)          (2*PI*k*t) ]
 f(t) = a+b*t+c*t*t + SUM  [ d * sin(--------) + e * cos(--------) ]
                       k=1 [  k     (    T   )    k     (    T   ) ]
```
where T = duration of time series; the a,b,c,d,e parameters are chosen to minimize the sum over t of |v(t)-f(t)| (L1 regression); this type of fitting is is insensitive to large spikes in the data. The default value of L is NT/30, where NT = number of time points.
Cut Values
Alter default values for the spike cut values [default c1=2.5, c2=4.0].
No Mask
Process all voxels [default=use a mask of high-intensity voxels,as created via '3dAutomask -dilate 4 dataset'].
Prefix
Save de-spiked dataset with a specified prefix [default=despike].
Save
Save 'spikiness' measure s for each voxel into a 3D+time dataset with specified prefix.

Important notes

Method:

	 * L1 fit a smooth-ish curve to each voxel time series
    		[see -corder option for description of the curve].
 	 * Compute the MAD of the difference between the curve and
    		the data time series (the residuals).
 	 * Estimate the standard deviation 'sigma' of the residuals
  		as sqrt(PI/2)*MAD.
 	 * For each voxel value, define s = (value-curve)/sigma.
	 * Values with s GREATER-THAN c1 are replaced with a value that yields
    		a modified s' = c1+(c2-c1)*tanh((s-c1)/(c2-c1)).
 	 * c1 is the threshold value of s for a 'spike' [default c1=2.5].
 	 * c2 is the upper range of the allowed deviation from the curve:
    		s=[c1..infinity) is mapped to s'=[c1..c2)   [default c2=4].

Caveats:

	* Despiking may interfere with image registration, since head
   		movement may produce 'spikes' at the edge of the brain, and
   		this information would be used in the registration process.
   		This possibility has not been explored.
	* Check your data visually before and after despiking and
   		registration!
   	[Hint: open 2 AFNI controllers, and turn Time Lock on.]

Last updated Wed Oct 29 18:43:09 EDT 2003