Usage: 3dUnifize [options] inputdataset
* The input dataset is supposed to be a T1-weighted volume,
possibly already skull-stripped (e.g., via 3dSkullStrip).
++ However, this program can be a useful step to take BEFORE
3dSkullStrip, since the latter program can fail if the input
volume is strongly shaded -- 3dUnifize will (mostly) remove
such shading artifacts.
* The output dataset has the white matter (WM) intensity approximately
uniformized across space, and scaled to peak at about 1000.
* The output dataset is always stored in float format!
* If the input dataset has more than 1 sub-brick, only sub-brick
#0 will be processed!
* If you have a lot of tissue inferior to the brain, you might have
to cut it off (using 3dZeropad -I -xxx to cut off the most inferior
xxx slices -- where you pick the number xxx visually), before
using 3dUnifize.
* Want to correct EPI datasets for nonuniformity?
You can try the new and experimental [Mar 2017] '-EPI' option.
* Method: Obi-Wan's personal variant of Ziad's sneaky trick.
(If you want to know what his trick is, you'll have to ask him, or
read Obi-Wan's source code [which is a world of ecstasy and exaltation],
or just read all the way to the end of this help output.)
* The principal motive for this program is for use in an image
registration script, and it may or may not be useful otherwise.
* This program replaces the older (and very different) 3dUniformize,
which is no longer maintained and may sublimate at any moment.
(In other words, we do not recommend the use of 3dUniformize.)
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Options:
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-prefix pp = Use 'pp' for prefix of output dataset.
-input dd = Alternative way to specify input dataset.
-T2 = Treat the input as if it were T2-weighted, rather than
T1-weighted. This processing is done simply by inverting
the image contrast, processing it as if that result were
T1-weighted, and then re-inverting the results.
++ This option is NOT guaranteed to be useful for anything!
++ Of course, nothing in AFNI comes with a guarantee :-)
++ If you want to be REALLY sneaky, giving this option twice
will skip the second inversion step, so the result will
look like a T1-weighted volume (except at the edges and
near blood vessels).
++ Might be useful for skull-stripping T2-weighted datasets.
++ Don't try the '-T2 -T2' trick on FLAIR-T2-weighted datasets.
The results aren't pretty!
-GM = Also scale to unifize 'gray matter' = lower intensity voxels
(to aid in registering images from different scanners).
++ For many datasets (especially those created by averaging),
using '-GM' will increase the WM-GM contrast somewhat;
however, that depends on the original WM-GM contrast.
++ This option is recommended for use with 3dQwarp when
aligning 2 T1-weighted volumes, in order to make the
WM-GM contrast about the same for the datasets, even
if they don't come from the same scanner/pulse-sequence.
++ Note that standardizing the contrasts with 3dUnifize will help
3dQwarp match the source dataset to the base dataset. If you
later want the original source dataset to be warped, you can
do so using the 3dNwarpApply program.
++ In particular, the template dataset MNI152_2009_template_SSW.nii.gz
(supplied with AFNI) has been treated with '-GM'. This dataset
is the one used by the @SSwarper script, so that script applies
3dUnifize with this '-GM' option to help with the alignment.
-Urad rr = Sets the radius (in voxels) of the ball used for the sneaky trick.
++ Default value is 18.3, and should be changed proportionally
if the dataset voxel size differs significantly from 1 mm.
-ssave ss = Save the scale factor used at each voxel into a dataset 'ss'.
++ This is the white matter scale factor, and does not include
the factor from the '-GM' option (if that was included).
++ The input dataset is multiplied by the '-ssave' image
(voxel-wise) to get the WM-unifized image.
++ Another volume (with the same grid dimensions) could be
scaled the same way using 3dcalc, if that is needed.
++ This saved scaled factor does NOT include any GM scaling :(
-amsave aa = Save the automask-ed input dataset.
++ This option and the previous one are used mostly for
figuring out why something peculiar happened, and are
otherwise useless.
-quiet = Don't print the fun fun fun progress messages (but whyyyy?).
++ For the curious, the codes used during this printout are:
A = Automask
D = Duplo down (process a half-size volume)
V = Voxel-wise histograms to get local scale factors
U = duplo Up (convert local scale factors to full-size volume)
W = multiply by White matter factors
G = multiply by Gray matter factors [cf -GM option]
I = contrast inversion [cf -T2 option]
M = compute median volume [cf -EPI option]
E = compute scaled EPI datasets [cf -EPI option]
[sXXX] = XXX voxel values were 'squashed' [cf -nosquash]
++ 'Duplo down' means to scale the input volume to be half the
grid size in each direction for speed when computing the
voxel-wise histograms. The sub-sampling is done using the
median of the central voxel value and its 6 nearest neighbors.
-noduplo = Do NOT use the 'duplo down' step; this can be useful for lower
resolution datasets.
++ If a dataset has less than 1 million voxels in a 3D volume,
'duplo down' will not be used in any case.
-EPI = Assume the input dataset is a T2 (or T2*) weighted EPI time
series. After computing the scaling, apply it to ALL volumes
(TRs) in the input dataset. That is, a given voxel will be
scaled by the same factor at each TR.
++ This option also implies '-noduplo' and '-T2'.
++ This option turns off '-GM' if you turned it on.
-->>++ This option is experimental; check your results!
++ Remember: the program tries to uniform-ize the White Matter
regions, so the overall appearance of the image may become
less uniform, especially if it was fairly uniform already.
++ For most purposes in AFNI processing, uniform-izing
EPI datasets is not needed.
-- If you are having trouble getting a good result from
3dAutomask, try adding the option '-clfrac 0.2'.
-- There is no reason to apply 3dUnifize to EPI datasets
that do not have significant shading artifacts.
-- EPI data from 7T systems might be 'improved' by 3dUnifize.
-- You might need to run 3dDespike before using 3dUnifize.
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Special options for Jedi AFNI Masters ONLY:
------------------------------------------
-rbt R b t = Specify the 3 parameters for the algorithm, as 3 numbers
following the '-rbt':
R = radius; same as given by option '-Urad' [default=18.3]
b = bottom percentile of normalizing data range [default=70.0]
r = top percentile of normalizing data range [default=80.0]
-T2up uu = Set the upper percentile point used for T2-T1 inversion.
The default value is 98.5 (for no good reason), and 'uu' is
allowed to be anything between 90 and 100 (inclusive).
++ The histogram of the data is built, and the uu-th percentile
point value is called 'U'. The contrast inversion is simply
given by output_value = max( 0 , U - input_value ).
-clfrac cc = Set the automask 'clip level fraction' to 'cc', which
must be a number between 0.1 and 0.9.
A small 'cc' means to make the initial threshold
for clipping (a la 3dClipLevel) smaller, which
will tend to make the mask larger. [default=0.1]
++ [22 May 2013] The previous version of this program used a
clip level fraction of 0.5, which proved to be too large
for some users, who had images with very strong shading issues.
Thus, the default value for this parameter was lowered to 0.1.
++ [24 May 2016] The default value for this parameter was
raised to 0.2, since the lower value often left a lot of
noise outside the head on non-3dSkullStrip-ed datasets.
You can still manually set -clfrac to 0.1 if you need to
correct for very large shading artifacts.
++ If the results of 3dUnifize have a lot of noise outside the head,
then using '-clfrac 0.5' (or even larger) will probably help.
++ If the results have 'hot spots' in the WM, also try setting
'-clfrac 0.5', which should help with this problem.
-nosquash = In Jan 2019, a change was made to 'squash' (reduce) large
values that sometimes occur in images - values larger than
typical WM intensities. For some applications, this procedure
does not produce images that are useful for 3dAllineate
(or so I was told, by people doing pig brain imaging).
This option will turn off the squashing step. [04 May 2020]
(I thought of calling it '-oink', but that would be)
(absurd, and as you know, Obi-Wan hates absurdity.)
++ If you want to know HOW the squashing is computed,
you know what Obi-Wan says: 'Trust in the Source, Luke'.
-- Feb 2013 - by Obi-Wan Unifobi
- can always be found at the Everest Bakery in Namche Bazaar,
if you have any questions about this program
-- This code uses OpenMP to speed up the slowest part (voxel-wise histograms).
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HOW IT WORKS (Ziad's sneaky trick is revealed at last! And more.)
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The basic idea is that white matter in T1-weighted images is reasonably
uniform in intensity, at least when averaged over 'large-ish' regions.
The first step is to create a local white matter intensity volume.
Around each voxel (inside the volume 'automask'), the ball of values
within a fixed radius (default=18.3 voxels) is extracted and these
numbers are sorted. The values in the high-intensity range of the
histogram (default=70% to 80%) are averaged. The result from this
step is a smooth 3D map of the 'white matter intensity' (WMI).
[The parameters of the above process can be altered with the '-rbt' option.]
[For speed, the WMI map is produced on an image that is half-size in all ]
[directions ('Duplo down'), and then is expanded back to the full-size ]
[volume ('Duplo up'). The automask procedure can be somewhat controlled ]
[via the '-clfrac' option. The default setting is designed to deal with ]
[heavily shaded images, where the WMI varies by a factor of 5 or more over ]
[the image volume. ]
The second step is to scale the value at every voxel location x in the input
volume by the factor 1000/WMI(x), so that the 'white matter intensity' is
now uniform-ized to be 1000 everywhere. (This is Ziad's 'trick'; it is easy,
works well, and doesn't require fitting some spatial model to the data: the
data provides its own model.)
If the '-GM' option is used, then this scaled volume is further processed
to make the lower intensity values (presumably gray matter) have a contrast
similar to that from a collection of 3 Tesla MP-RAGE images that were
acquired at the NIH. (This procedure is not Ziad's fault, and should be
blamed on the reclusive Obi-Wan Unifobi.)
From the WM-uniform-ized volume, the median of all values larger than 1000
is computed; call this value P. P-1000 represents the upward dispersion
of the high-intensity (white matter) voxels in the volume. This value is
'reflected' below 1000 to Q = 1000 - 2*(P-1000), and Q is taken to be the
upper bound for gray matter voxel intensities. A lower bound for gray
matter voxel values is estimated via the 'clip fraction' algorithm as
implemented in program 3dClipLevel; call this lower bound R. The median
of all values between R and Q is computed; call this value G, which is taken
to be a 'typical' gray matter voxel instensity. Then the values z in the
entire volume are linearly scaled by the formula
z_out = (1000-666)/(1000-G) * (z_in-1000) + 1000
so that the WM uniform-ized intensity of 1000 remains at 1000, and the gray
matter median intensity of G is mapped to 666. (Values z_out that end up
negative are set to 0; as a result, some of CSF might end up as 0.)
The value 666 was chosen because it gave results visually comparable to
various NIH-generated 3 Tesla T1-weighted datasets. (Any suggestions that
this value was chosen for other reasons will be treated as 'beastly'.)
To recap: the WM uniform-ization process provides a linear scaling factor
that varies for each voxel ('local'), while the GM normalization process
uses a global linear scaling. The GM process is optional, and is simply
designed to make the various T1-weighted images look similar.
-----** CAVEAT **-----
This procedure was primarily developed to aid in 3D registration, especially
when using 3dQwarp, so that the registration algorithms are trying to match
images that are alike. It is *NOT* intended to be used for quantification
purposes, such as Voxel Based Morphometry! That would better be done via
the 3dSeg program, which is far more complicated.
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++ Compile date = Dec 17 2024 {AFNI_24.3.10:linux_ubuntu_24_64}
