This script has dual purposes for processing a given subject's
anatomical volume:
    + to skull-strip the brain, and
    + to calculate the warp to a reference template/standard space.
Automatic snapshots of the registration are created, as well, to help
the QC process.

This program cordially ties in directly with afni_proc.py, so you can
run it beforehand, check the results, and then provide both the
skull-stripped volume and the warps to the processing program.  That
is convenient.

Current version = 2.6
Authorship      = Bob, Bob, there is one Bob, He spells it B-O-B.

# -----------------------------------------------------------------


    @SSwarper             \
        -input  AA           \
        -base   BB           \
        -subid  SS           \
       {-odir   OD}          \
       {-minp   MP}          \
       {-nolite}             \
       {-skipwarp}           \
       {-unifize_off}        \
       {-init_skullstr_off}  \
       {-extra_qc_off}       \
       {-jump_to_extra_qc}   \
       {-cost_nl_init CNI}   \
       {-cost_nl_final CNF}  \
       {-deoblique}          \
       {-deoblique_refitly}  \
       {-warpscale WS}       \
       {-SSopt 'strings'     \
       {-aniso_off}          \
       {-ceil_off}           \
       {-tmp_name_nice}      \
       {-echo}               \
       {-verb}               \

where (note: many of the options with 'no' and 'off' in their name are
really just included for backwards compatibility, as this program has
grown/improved over time):

  -input  AA :(req) an anatomical dataset, *not* skull-stripped, with
              resolution about 1 mm.

  -base   BB :(req) a base template dataset, with contrast similar to
              the input AA dset, probably from some kind of standard
              NB: this dataset is not *just* a standard template,
              because it is not a single volume-- read about its
              composition in the NOTES on the 'The Template Dataset',
              The program first checks if the dset BB exists as
              specified; if not, then if just the filename has been
              provided it searches the AFNI_GLOBAL_SESSION,
              AFNI_PLUGINPATH, and afni bin directory (in that order)
              for the named dataset.

  -subid  SS :(req) name code for output datasets (e.g., 'sub007').

  -odir   OD :(opt) output directory for all files from this program
              (def: directory of the '-input AA').

  -minp   MP :(opt) minimum patch size on final 3dQwarp (def: 11).

  -nolite    :(opt) Do not use the '-lite' option with 3dQwarp;
              This option is used for backward compatibility, if you want
              to run 3dQwarp the same way as older versions of @SSwarper.
              The new way (starting Jan 2019) is to use the '-lite'
              option with 3dQwarp to speed up the calculations.
              (def: use '-lite' for faster calculations).

  -skipwarp  :(opt) Do not compute past the output of anatSS.{subid}.nii.
              This option is used if you just want the skull-stripped
              result in original coordinates, without the warping
              to the template space (anatQQ). The script will run faster.

  -deoblique :(opt) apply obliquity information to deoblique the input
              volume ('3dWarp -deoblique -wsinc5 ...'), as an initial step.
              This might introduce the need to overcome a large rotation
              during the alignment, though

  -deoblique_refitly :(opt) purge obliquity information to deoblique
              the input volume (copy, and then '3drefit -deoblique ...'),
              as an initial step.  This might help when data sets are
              very... oblique.

  -warpscale WS :(opt) opt to control flexibility of warps in 3dQwarp and
              how they adjust with patch size;  see 3dQwarp's help for
              more info. Allowed values of WS are in range [0.1, 1.0].
              (def: 1.0)

  -giant_move :(opt) when starting the initial alignment to the template,
              apply the same parameter expansions to 3dAllineate that
              align_epi_anat.py does with the same option flag.  This
              might be useful if the brain has a very large angle away
              from "typical" ones, etc.

  -unifize_off :(opt) don't start with a 3dUnifize command to try reduce
              effects of brightness inhomogeneities.  Probably only
              useful if unifizing has been previously performed on the
              input dset.

  -aniso_off :(opt) don't preprocess with a 3danisosmooth command to
              try reduce effects of weird things (in a technical
              sense).  Possible that this will never be used in the
              history of running this program.

  -ceil_off  :(opt) by default, after anisosmoothing, this program
              will apply put a ceiling on values in the dset, to get rid
              of possible outliers (ceil = 98%ile of non-zero voxels in
              the whole volume).  This option will turn that off.

  -init_skullstr_off :(opt) don't preprocess with a 3dSkullstrip command
              to roughly isolated brain in the beginning.  This might
              be useful with macaque dsets.

  -extra_qc_off :(opt) don't make extra QC images QC*jpg (for some
              unknown reason).

  -jump_to_extra_qc :(opt) just make the two QC*jpg images from a
              previous run of @SSwarper.  These QC*jpg images are new
              QC output (as of late Feb, 2020), so this might be
              useful to add a quick check to previously run data.
              This command would just be tacked on to previously
              executed one.

  #-cost_aff CA :***no longer used.*** The affine cost function is only
              set via cost_nl_init, since the affine alignment is just a
              'preliminary alignment' for that one.  So, what is specified
              for the cost_nl_init will be used for the affine.

  -cost_nl_init CNI
             :(opt) specify cost function for initial nonlinear
              (3dQwarp) part of alignment.  Here, 'CNI' would be the
              cost function name to be provided (def: is now "lpa").
              This is probably only here for backwards compatibility
              to older @SSwarper (where def was 'pcl').

  -cost_nl_final CNF
             :(opt) specify cost function for final nonlinear
              (3dQwarp) parts of alignment.  Here, 'CNF' would be the
              cost function to be provided (def: is now "pcl").  This
              is separate from the initial nonlinear warp cost values
              '-cost_nl_init ..', because using those here might be
              pretty slow; however, using "lpa" here might help

  -SSopt 'strings' :(opt) The content of 'strings' (which should be
              in quotes if there are any blanks) is copied to the
              end of the 3dSkullStrip command line. Example:
                -SSopt '-o_ply Fred.Is.Wonderful'
              to have 3dSkullStrip produce a .ply surface file
              as an additional output.

  -mask_ss MSS :(opt) as an alternative to skullstripping at an early
              stage, you can provide a mask to be used before the
              initial affine alignment.  The mask MSS can come from
              anywhere, but @SUMA_Make_Spec_FS now makes a convenient
              one from the FS parcellation (though it would have to be
              resampled to the input anatomical's grid).

  -tmp_name_nice :(opt) default temporary "junk.*" filenames include
              a large, random char string.  This is ugly, but useful
              if outputting several different SSW runs into the same
              directory that we intermediate files (very likely) don't
              get overwritten.  However, if you prefer, you can use a
              nicer, non-random intermediate file prefix: "junk_ssw".
              I would use this when the output dir ("-odir ..")
              doesn't contain multiple SSW outputs.

  -verb      :(opt) Apply the '-verb' option to 3dQwarp, to get more
              verbose progress information - mostly used for debugging.

  -echo      :(opt) Run the script with "set echo", for extra verbosity
              in the terminal output.  Mainly for debugging times.

  -noclean   :(opt) Do not delete the 'junk' files at the end of
              computations - mostly used for debugging and testing.

# -----------------------------------------------------------------


If you are reading this message, then several reference data sets
(base volumes) for @SSwarper now exist within the AFNI realm. Oh, what
a time it is to be alive.  A current list includes:

+ MNI152_2009_template_SSW.nii.gz
+ TT_N27_SSW.nii.gz
+ HaskinsPeds_NL_template1.0_SSW.nii.gz

Some of these are distributed with the AFNI binaries, and other may be
found online. You can make other reference base templates in whatever
space you prefer, but note that it must have several subvolumes of
information included-- see NOTES on the 'The Template Dataset', below
(which also contains a link to the @SSwarper template tutorial online

# ----------------------------------------------------------------------



Suppose the -prefix is 'sub007' (because you scanned Bond, JamesBond?).
Then the outputs from this script will be"

anatDO.sub007.nii       = deobliqued version of original dataset;
                          (*only if* using '-deoblique' opt);
anatU.sub007.nii        = intensity uniform-ized original dataset
                          (or, if '-unifize_off' used, a copy of orig dset);
anatUA.sub007.nii       = anisotropically smoothed version of the above
                          (or, if '-aniso_off' used, a copy of anatU.*.nii)
anatUAC.sub007.nii      = ceiling-capped ver of the above (at 98%ile of
                          non-zero values)
                          (or, if '-ceil_off' used, a copy of anatUA.*.nii)

anatS.sub007.nii        = first pass skull-stripped original dataset
                          (or, if '-init_skullstr_off' used, a copy of
anatSS.sub007.nii       = second pass skull-stripped original dataset;
                          * note that anatS and anatSS are 'original'
                            in the sense that they are aligned with
                            the input dataset - however, they have been
                            unifized and weakly smoothed: they are
                            stripped versions of anatUAC; if you want
                            a skull-stripped copy of the input with
                            no other processing, use a command like
                              3dcalc -a INPUTDATASET        \
                                     -b anatSS.sub007.nii   \
                                     -expr 'a*step(b)'      \
                                     -prefix anatSSorig.sub007.nii

anatQQ.sub007.nii       = skull-stripped dataset nonlinearly warped to
                          the base template space;
anatQQ.sub007.aff12.1D  = affine matrix to transform original dataset
                          to base template space;
anatQQ.sub007_WARP.nii  = incremental warp from affine transformation
                          to nonlinearly aligned dataset;

* The .aff12.1D and _WARP.nii transformations need to be catenated to get
  the full warp from original space to the base space; example:

    3dNwarpApply -nwarp 'anatQQ.sub007_WARP.nii anatQQ.sub007.aff12.1D' ...

QC images

  AMsub007.jpg            = 3x3 snapshot image of the anatQQ.sub007.nii
                            dataset with the edges from the base template
                            overlaid -- to check the alignment;
  MAsub007.jpg            = similar to the above, with the roles of the
                            template and the anatomical datasets reversed.
  QC_anatQQ.sub007.jpg    = like AM*.jpg, but 3 rows of 8 slices
  QC_anatSS.sub007.jpg    = check skullstripping in orig space: ulay is
                            input dset, and olay is mask of
                            skullstripped output (anatSS* dset)

    o [ulay] original source dset
      [olay] original base dset
    o single image montage to check initial overlap of source and base,
      ignoring any obliquity that might be present (i.e., the way AFNI
      GUI does by default, and also how alignment starts)
    o if initial overlap is not strong, alignment can fail or
      produce weirdness
    o *if* either dset has obliquity, then an image of both after
      deobliquing with 3dWarp is created (*DEOB.jpg), and a text file
      about obliquity is also created (*DEOB.txt).

* It is important to examine (at least) the two .jpg snapshot images to
  make sure that the skull-stripping and nonlinear warping worked well.


When B-O-B uses @SSwarper for skull-stripping plus warping, He gives
afni_proc.py these options (among others, hence the ellipses), after
running @SSwarper successfully. Here, 'subj' is the subject

|    set template = MNI152_2009_template_SSW.nii.gz
|    afni_proc.py                                                  \
|      ...                                                         \
|      -copy_anat             anatSS.${subj}.nii                   \
|      -anat_has_skull        no                                   \
|      -align_opts_aea        -cost lpc+ZZ -giant_move             \
|                             -check_flip                          \
|      -volreg_align_to       MIN_OUTLIER                          \
|      -volreg_align_e2a                                           \
|      -volreg_tlrc_warp                                           \
|      -tlrc_base             ${template}                          \
|      -tlrc_NL_warp                                               \
|      -tlrc_NL_warped_dsets  anatQQ.${subj}.nii                   \
|                             anatQQ.${subj}.aff12.1D              \
|                             anatQQ.${subj}_WARP.nii
|      ...


The Template dataset

Any reference base template dataset, such as
MNI152_2009_template_SSW.nii.gz, must have the first *4* volumes here
(and can have the optional 5th for later uses, as described):
  [0] = skull-stripped template brain volume
  [1] = skull-on template brain volume
  [2] = weight mask for nonlinear registration, with the
        brain given greater weight than the skull
  [3] = binary mask for the brain
  [4] = binary mask for gray matter plus some CSF (slightly dilated)
        ++ this volume is not used in this script
        ++ it is intended for use in restricting FMRI analyses
           to the 'interesting' parts of the brain
        ++ this mask should be resampled to your EPI spatial
           resolution (see program 3dfractionize), and then
           combined with a mask from your experiment reflecting
           your EPI brain coverage (see program 3dmask_tool).

More information about making these (with scripts) is provided on
the Interweb:

The steps being run

You Know My Methods, Watson...

#1: Uniform-ize the input dataset's intensity via 3dUnifize.
     ==> anatU.sub007.nii
#2: Strip the skull with 3dSkullStrip, with mildly aggressive settings.
     ==> anatS.sub007.nii
#3: Nonlinearly warp (3dQwarp) the result from #1 to the skull-on
    template, driving the warping to a medium level of refinement.
#4: Use a slightly dilated brain mask from the template to
    crop off the non-brain tissue resulting from #3 (3dcalc).
#5: Warp the output of #4 back to original anatomical space,
    along with the template brain mask, and combine those
    with the output of #2 to get a better skull-stripped
    result in original space (3dNwarpApply and 3dcalc).
     ==> anatSS.sub007.nii
#6  Restart the nonlinear warping, registering the output
    of #5 to the skull-off template brain volume (3dQwarp).
     ==> anatQQ.sub007.nii (et cetera)
#7  Use @snapshot_volreg to make the pretty pictures.
     ==> AMsub007.jpg and MAsub007.jpg

Temporary files

If the script crashes for some reason, it might leave behind files
whose names start with 'junk' -- you should delete these files


The importance of initial dset overlap

Always, always, always check the initial image made by SSW when it


This image tells you how well your datasets overlap initially before
the alignment work begins. **The better the overlap, the lower the
chance that something weird happens in your output.** All the SSW
templates have reasonable coordinates, meaning that (x, y, z) = (0,
0, 0) is in a good spot for it.  If there is poor overlap, probably
your input dataset has weird/bad coordinates for some reason.

You can use @Align_Centers to put your anatomical dset in a better
spot (though note, if you are going to be processing EPI data
afterwards, you will want to move that along, as well, perhaps as a
"child" dataset).

By far the most common problem leading to obviously bad outputs is
that the initial datasets are waaay far apart when they start, and
the program gets stuck in a false minimum of solutions.

Other issues

Sometimes, it can be hard to separate the brain from dura and/or
skull surrounding the brain.  If little bits are left around in the
masking images, then perhaps adding one of the following options for
will help (this can help the initial skullstripping):

  -SSopt '-blur_fwhm 2'
  -SSopt '-blur_fwhm 3'

Any other questions/oddities, please don't hesitate to inquire on
the AFNI Message Board


1) Run the program, deciding what the main output directory will be
called (e.g., based on the subject ID):

  @SSwarper                                    \
      -input  anat_t1w.nii.gz                  \
      -base   MNI152_2009_template_SSW.nii.gz  \
      -subid  sub-001                          \
      -odir   group/o.aw_sub-001

2) Same as above, but since we are using one outdir per subject, use
more aesthetically pleasing names of temporary files (which get
deleted, anyways):

  @SSwarper                                    \
      -tmp_name_nice                           \
      -input  anat_t1w.nii.gz                  \
      -base   MNI152_2009_template_SSW.nii.gz  \
      -subid  sub-001                          \
      -odir   group/o.aw_sub-001

3) As of version 2.5, you can input a mask to be used instead of
skullstripping.  For example, a good one might be the
parcellation-derived (but filled in) mask from @SUMA_Make_Spec_FS
after running FS's recon-all (though you will have to resample it
from the FS output grid to that of your input anatomical):

  @SSwarper                                     \
      -tmp_name_nice                            \
      -input   anat_t1w.nii.gz                  \
      -mask_ss fs_parc_wb_mask_RES.nii.gz       \
      -base    MNI152_2009_template_SSW.nii.gz  \
      -subid   sub-001                          \
      -odir    group/o.aw_sub-001