AFNI program: 3dDespike

Usage: 3dDespike [options] dataset
Removes 'spikes' from the 3D+time input dataset and writes
a new dataset with the spike values replaced by something
more pleasing.

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 > 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].

Options:
 -ignore I  = Ignore the first I points in the time series:
               these values will just be copied to the
               output dataset [default I=0].
 -corder L  = 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 c1 c2 = Alter default values for the spike cut values
               [default c1=2.5, c2=4.0].
 -prefix pp = Save de-spiked dataset with prefix 'pp'
               [default pp='despike']
 -ssave ttt = Save 'spikiness' measure s for each voxel into a
               3D+time dataset with prefix 'ttt' [default=no save]
 -nomask    = Process all voxels
               [default=use a mask of high-intensity voxels, ]
               [as created via '3dAutomask -dilate 4 dataset'].

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.]