7.1.70. 3dDespikeΒΆ

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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 to the eye.

Method:
  • L1 fit a smooth-ish curve to each voxel time series
[see -corder option for description of the curve]
[see -NEW option for a different & faster fitting method]
  • 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’].
-dilate nd = Dilate ‘nd’ times (as in 3dAutomask). The default
value of ‘nd’ is 4.
-q[uiet] = Don’t print ‘++’ informational messages.
-localedit = Change the editing process to the following:
If a voxel |s| value is >= c2, then replace the voxel value with the average of the two nearest non-spike (|s| < c2) values; the first one previous and the first one after. Note that the c1 cut value is not used here.
-NEW
= Use the ‘new’ method for computing the fit, which

should be faster than the L1 method for long time series (200+ time points); however, the results are similar but NOT identical. [29 Nov 2013] * You can also make the program use the ‘new’

method by setting the environment variable
AFNI_3dDespike_NEW
to the value YES; as in
setenv AFNI_3dDespike_NEW YES (csh) export AFNI_3dDespike_NEW=YES (bash)
  • If this variable is set to YES, you can turn off the ‘-NEW’ processing by using the ‘-OLD’ option.
–>>* For time series more than 500 points long, the
‘-OLD’ algorithm is tremendously slow. You should use the ‘-NEW’ algorith in such cases.
** At some indeterminate point in the future, the ‘-NEW’
method will become the default!

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 or calibrated.
  • [LATER] Actually, it seems like the registration problem

    does NOT happen, and in fact, despiking seems to help!

  • Check your data visually before and after despiking and

    registration! [Hint: open 2 AFNI controllers, and turn Time Lock on.]

  • This binary version of 3dDespike is compiled using OpenMP, a semi-

    automatic parallelizer software toolkit, which splits the work across multiple CPUs/cores on the same shared memory computer.

  • OpenMP is NOT like MPI – it does not work with CPUs connected only

    by a network (e.g., OpenMP doesn’t work with ‘cluster’ setups).

  • For implementation and compilation details, please see

    http://afni.nimh.nih.gov/pub/dist/doc/misc/OpenMP.html

  • The number of CPU threads used will default to the maximum number on

    your system. You can control this value by setting environment variable OMP_NUM_THREADS to some smaller value (including 1).

  • Un-setting OMP_NUM_THREADS resets OpenMP back to its default state of

    using all CPUs available. ++ However, on some systems (such as the NIH Biowulf), it seems to be

    necessary to set OMP_NUM_THREADS explicitly, or you only get one CPU.

    ++ On other systems with many CPUS, you probably want to limit the CPU

    count, since using more than (say) 16 threads is probably useless.

  • You must set OMP_NUM_THREADS in the shell BEFORE running the program,

    since OpenMP queries this variable BEFORE the program actually starts. ++ You can’t usefully set this variable in your ~/.afnirc file or on the

    command line with the ‘-D’ option.

  • How many threads are useful? That varies with the program, and how well

    it was coded. You’ll have to experiment on your own systems!

  • The number of CPUs on this particular computer system is ...... 16.

  • The maximum number of CPUs that will be used is now set to .... 7.

++ Compile date = Dec 16 2015

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