Usage: 3danisosmooth [options] dataset
Smooths a dataset using an anisotropic smoothing technique.

The output dataset is preferentially smoothed to preserve edges.

Options :
  -prefix pname = Use 'pname' for output dataset prefix name.
  -iters nnn = compute nnn iterations (default=10)
  -2D = smooth a slice at a time (default)
  -3D = smooth through slices. Can not be combined with 2D option
  -mask dset = use dset as mask to include/exclude voxels
  -automask = automatically compute mask for dataset
    Can not be combined with -mask
  -viewer = show central axial slice image every iteration.
    Starts aiv program internally.
  -nosmooth = do not do intermediate smoothing of gradients
  -sigma1 n.nnn = assign Gaussian smoothing sigma before
    gradient computation for calculation of structure tensor.
    Default = 0.5
  -sigma2 n.nnn = assign Gaussian smoothing sigma after
    gradient matrix computation for calculation of structure tensor.
    Default = 1.0
  -deltat n.nnn = assign pseudotime step. Default = 0.25
  -savetempdata = save temporary datasets each iteration.
    Dataset prefixes are Gradient, Eigens, phi, Dtensor.
    Ematrix, Flux and Gmatrix are also stored for the first sub-brick.
    Where appropriate, the filename is suffixed by .ITER where
    ITER is the iteration number. Existing datasets will get overwritten.
  -save_temp_with_diff_measures: Like -savetempdata, but with
    a dataset named Diff_measures.ITER containing FA, MD, Cl, Cp,
    and Cs values.
  -phiding = use Ding method for computing phi (default)
  -phiexp = use exponential method for computing phi
  -noneg = set negative voxels to 0
  -setneg NEGVAL = set negative voxels to NEGVAL
  -edgefraction n.nnn = adjust the fraction of the anisotropic
    component to be added to the original image. Can vary between
    0 and 1. Default =0.5
  -datum type = Coerce the output data to be stored as the given type
    which may be byte, short or float. [default=float]
  -matchorig - match datum type and clip min and max to match input data
  -help = print this help screen

  Z Ding, JC Gore, AW Anderson, Reduction of Noise in Diffusion
   Tensor Images Using Anisotropic Smoothing, Mag. Res. Med.,
   53:485-490, 2005
  J Weickert, H Scharr, A Scheme for Coherence-Enhancing
   Diffusion Filtering with Optimized Rotation Invariance,
   CVGPR Group Technical Report at the Department of Mathematics
   and Computer Science,University of Mannheim,Germany,TR 4/2000.
  J.Weickert,H.Scharr. A scheme for coherence-enhancing diffusion
   filtering with optimized rotation invariance. J Visual
   Communication and Image Representation, Special Issue On
   Partial Differential Equations In Image Processing,Comp Vision
   Computer Graphics, pages 103-118, 2002.
  Gerig, G., Kubler, O., Kikinis, R., Jolesz, F., Nonlinear
   anisotropic filtering of MRI data, IEEE Trans. Med. Imaging 11
   (2), 221-232, 1992.

This program accepts datasets that are modified on input according to the
following schemes:
  'r1+orig[3..5]'                                    {sub-brick selector}
  'r1+orig<100..200>'                                {sub-range selector}
  'r1+orig[3..5]<100..200>'                          {both selectors}
  '3dcalc( -a r1+orig -b r2+orig -expr 0.5*(a+b) )'  {calculation}
For the gruesome details, see the output of 'afni -help'.

++ Compile date = Aug 10 2020 {AFNI_20.2.11:linux_ubuntu_16_64}