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.
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Outline of Method:
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* 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
from the 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].
An alternative method for replacing the spike value is provided
by the '-localedit' option, and that method is preferred by
many users.
The input dataset can be stored in short or float formats.
The output dataset will always be stored in floats. [Feb 2017]
--------
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' algorithm in such cases.
** At some indeterminate point in the future, the '-NEW'
method will become the default!
-->>* As of 29 Sep 2016, '-NEW' is the default if there
is more than 500 points in the time series dataset.
-NEW25 = A slightly more aggressive despiking approach than
the '-NEW' method.
--------
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!
=========================================================================
* 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 across cluster nodes).
* For some implementation and compilation details, please see
https://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, it seems to be necessary to set variable
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 ...... 1.
* The maximum number of CPUs that will be used is now set to .... 1.
=========================================================================
++ Compile date = Dec 17 2024 {AFNI_24.3.10:linux_ubuntu_24_64}
