Provided by: mia-tools_2.4.6-4ubuntu2_amd64 bug

NAME

       ('mia\-2dmyoica\-full',) - Run a registration of a series of 2D images.

SYNOPSIS

       mia-2dmyoica-full -i <in-file> -o <out-file> [options]

DESCRIPTION

       mia-2dmyoica-full  This  program  implements  the  2D  version  of the motion compensation
       algorithm described in Wollny G, Kellman P,  Santos  A,  Ledesma-Carbayo  M-J,  "Automatic
       Motion  Compensation  of  Free  Breathing  acquired  Myocardial  Perfusion  Data  by using
       Independent     Component     Analysis",      Medical      Image      Analysis,      2012,
       DOI:10.1016/j.media.2012.02.004. The software may first run a linear registration and then
       a non-linear registration or just one of the both.This version of the program may run  all
       registrations in parallel.

OPTIONS

   File-IO
              -i --in-file=(input, required); string
                     input perfusion data set

              -o --out-file=(output, required); string
                     output perfusion data set

              -r --registered=
                     File  name  base  for the registered images. Image type and numbering scheme
                     are taken from the input images as given in the input data set.

                 --save-cropped=(output); string
                     save cropped set to this file, the image files will use the stem of the name
                     as file name base

                 --save-feature=(output); string
                     save segmentation feature images and initial ICA mixing matrix

                 --save-refs=(output); string
                     for each registration pass save the reference images to files with the given
                     name base

                 --save-regs=(output); string
                     for each registration pass save intermediate registered images

   Help & Info
              -V --verbose=warning
                     verbosity of output, print messages of given level  and  higher  priorities.
                     Supported priorities starting at lowest level are:

                        info ‐ Low level messages
                        trace ‐ Function call trace
                        fail ‐ Report test failures
                        warning ‐ Warnings
                        error ‐ Report errors
                        debug ‐ Debug output
                        message ‐ Normal messages
                        fatal ‐ Report only fatal errors

                 --copyright
                     print copyright information

              -h --help
                     print this help

              -? --usage
                     print a short help

                 --version
                     print the version number and exit

   ICA
                 --fastica=internal
                     FastICA implementationto be used
                      For supported plugins see PLUGINS:fastica/implementation

              -C --components=0
                     ICA components 0 = automatic estimation

                 --normalize
                     normalized ICs

                 --no-meanstrip
                     don't strip the mean from the mixing curves

              -s --segscale=0
                     segment and scale the crop box around the LV (0=no segmentation)

              -k --skip=0
                     skip images at the beginning of the series e.g. because as they are of other
                     modalities

              -m --max-ica-iter=400
                     maximum number of iterations in ICA

              -E --segmethod=features
                     Segmentation method

                        delta-peak ‐ difference of the peak enhancement images
                        features ‐ feature images
                        delta-feature ‐ difference of the feature images

              -b --min-breathing-frequency=-1
                     minimal mean frequency a mixing curve can have to be considered to stem from
                     brething.  A  healthy  rest breating rate is 12 per minute. A negative value
                     disables the test. A value  0.0  forces  the  series  to  be  identified  as
                     acquired with initial breath hold.

   Processing
                 --threads=-1
                     Maxiumum number of threads to use for processing,This number should be lower
                     or equal to the number of logical  processor  cores  in  the  machine.  (-1:
                     automatic estimation).

   Registration
              -L --linear-optimizer=gsl:opt=simplex,step=1.0
                     Optimizer  used for minimization of the linear registration The string value
                     will be used to construct a plug-in.
                      For supported plugins see PLUGINS:minimizer/singlecost

                 --linear-transform=affine
                     linear transform to be used The string value will be  used  to  construct  a
                     plug-in.
                      For supported plugins see PLUGINS:2dimage/transform

              -O --non-linear-optimizer=gsl:opt=gd,step=0.1
                     Optimizer  used  for minimization in the non-linear registration. The string
                     value will be used to construct a plug-in.
                      For supported plugins see PLUGINS:minimizer/singlecost

              -a --start-c-rate=16
                     start coefficinet rate in spines,  gets  divided  by  --c-rate-divider  with
                     every pass.

                 --c-rate-divider=2
                     Cofficient rate divider for each pass.

              -d --start-divcurl=10000
                     Start divcurl weight, gets divided by --divcurl-divider with every pass.

                 --divcurl-divider=2
                     Divcurl weight scaling with each new pass.

              -R --reference=-1
                     Global  reference  all  image should be aligned to. If set to a non-negative
                     value, the images will be aligned to this references, and the cropped output
                     image  date  will  be  injected into the original images. Leave at -1 if you
                     don't care. In this case all images with be registered to a mean position of
                     the movement

              -w --imagecost=image:weight=1,cost=ssd
                     image  cost, do not specify the src and ref parameters, these will be set by
                     the program. The string value will be used to construct a plug-in.
                      For supported plugins see PLUGINS:2dimage/fullcost

              -l --mg-levels=3
                     multi-resolution levels

              -p --linear-passes=3
                     linear registration passes (0 to disable)

              -P --nonlinear-passes=3
                     non-linear registration passes (0 to disable)

PLUGINS: 1d/splinebc

       mirror    Spline interpolation boundary conditions that mirror on the boundary

                     (no parameters)

       repeat    Spline interpolation boundary conditions that repeats the value at the boundary

                     (no parameters)

       zero      Spline interpolation boundary conditions that assumes zero for values outside

                     (no parameters)

PLUGINS: 1d/splinekernel

       bspline   B-spline kernel creation , supported parameters are:

                     d = 3; int in [0, 5]
                       Spline degree.

       omoms     OMoms-spline kernel creation, supported parameters are:

                     d = 3; int in [3, 3]
                       Spline degree.

PLUGINS: 2dimage/cost

       lncc      local normalized cross correlation with masking support.,  supported  parameters
                 are:

                     w = 5; uint in [1, 256]
                       half  width  of  the  window  used  for  evaluating  the  localized  cross
                       correlation.

       lsd       Least-Squares Distance measure

                     (no parameters)

       mi        Spline parzen based mutual information., supported parameters are:

                     cut = 0; float in [0, 40]
                       Percentage of pixels  to  cut  at  high  and  low  intensities  to  remove
                       outliers.

                     mbins = 64; uint in [1, 256]
                       Number of histogram bins used for the moving image.

                     mkernel = [bspline:d=3]; factory
                       Spline  kernel for moving image parzen hinstogram.  For supported plug-ins
                       see PLUGINS:1d/splinekernel

                     rbins = 64; uint in [1, 256]
                       Number of histogram bins used for the reference image.

                     rkernel = [bspline:d=0]; factory
                       Spline kernel for reference image parzen hinstogram.  For supported  plug-
                       ins see PLUGINS:1d/splinekernel

       ncc       normalized cross correlation.

                     (no parameters)

       ngf       This  function  evaluates  the  image  similarity  based  on normalized gradient
                 fields. Various evaluation kernels are available., supported parameters are:

                     eval = ds; dict
                       plugin subtype.  Supported values are:
                           sq ‐ square of difference
                           ds ‐ square of scaled difference
                           dot ‐ scalar product kernel
                           cross ‐ cross product kernel

       ssd       2D imaga cost: sum of squared differences, supported parameters are:

                     autothresh = 0; float in [0, 1000]
                       Use automatic masking of the moving image by only takeing intensity values
                       into accound that are larger than the given threshold.

                     norm = 0; bool
                       Set whether the metric should be normalized by the number of image pixels.

       ssd-automask
                 2D  image  cost:  sum  of  squared  differences, with automasking based on given
                 thresholds, supported parameters are:

                     rthresh = 0; double
                       Threshold intensity value for reference image.

                     sthresh = 0; double
                       Threshold intensity value for source image.

PLUGINS: 2dimage/fullcost

       image     Generalized image similarity cost function that  also  handles  multi-resolution
                 processing.  The  actual  similarity  measure  is  given  es  extra  parameter.,
                 supported parameters are:

                     cost = ssd; factory
                       Cost function kernel.  For supported plug-ins see PLUGINS:2dimage/cost

                     debug = 0; bool
                       Save intermediate resuts for debugging.

                     ref =(input, io)
                       Reference image.  For supported file types see PLUGINS:2dimage/io

                     src =(input, io)
                       Study image.  For supported file types see PLUGINS:2dimage/io

                     weight = 1; float
                       weight of cost function.

       labelimage
                 Similarity cost function that maps labels  of  two  images  and  handles  label-
                 preserving multi-resolution processing., supported parameters are:

                     debug = 0; int in [0, 1]
                       write the distance transforms to a 3D image.

                     maxlabel = 256; int in [2, 32000]
                       maximum number of labels to consider.

                     ref =(input, io)
                       Reference image.  For supported file types see PLUGINS:2dimage/io

                     src =(input, io)
                       Study image.  For supported file types see PLUGINS:2dimage/io

                     weight = 1; float
                       weight of cost function.

       maskedimage
                 Generalized  masked  image  similarity  cost  function  that also handles multi-
                 resolution processing. The provided masks should be  densly  filled  regions  in
                 multi-resolution  procesing  because otherwise the mask information may get lost
                 when downscaling the image. The reference mask and the transformed mask  of  the
                 study  image  are combined by binary AND. The actual similarity measure is given
                 es extra parameter., supported parameters are:

                     cost = ssd; factory
                       Cost     function     kernel.      For     supported     plug-ins      see
                       PLUGINS:2dimage/maskedcost

                     ref =(input, io)
                       Reference image.  For supported file types see PLUGINS:2dimage/io

                     ref-mask =(input, io)
                       Reference   image   mask    (binary).    For   supported  file  types  see
                       PLUGINS:2dimage/io

                     src =(input, io)
                       Study image.  For supported file types see PLUGINS:2dimage/io

                     src-mask =(input, io)
                       Study   image   mask   (binary).    For   supported   file    types    see
                       PLUGINS:2dimage/io

                     weight = 1; float
                       weight of cost function.

PLUGINS: 2dimage/io

       bmp       BMP  2D-image  input/output support. The plug-in supports reading and writing of
                 binary images and 8-bit gray scale images. read-only  support  is  provided  for
                 4-bit  gray  scale  images.  The color table is ignored and the pixel values are
                 taken as literal gray scale values.

                     ('Recognized file extensions: ', '.BMP, .bmp')

                     Supported element types:
                       binary data, unsigned 8 bit

       datapool  Virtual IO to and from the internal data pool

                     ('Recognized file extensions: ', '.@')

       dicom     2D image io for DICOM

                     ('Recognized file extensions: ', '.DCM, .dcm')

                     Supported element types:
                       signed 16 bit, unsigned 16 bit

       exr       a 2dimage io plugin for OpenEXR images

                     ('Recognized file extensions: ', '.EXR, .exr')

                     Supported element types:
                       unsigned 32 bit, floating point 32 bit

       jpg       a 2dimage io plugin for jpeg gray scale images

                     ('Recognized file extensions: ', '.JPEG, .JPG, .jpeg, .jpg')

                     Supported element types:
                       unsigned 8 bit

       png       a 2dimage io plugin for png images

                     ('Recognized file extensions: ', '.PNG, .png')

                     Supported element types:
                       binary data, unsigned 8 bit, unsigned 16 bit

       raw       RAW 2D-image output support

                     ('Recognized file extensions: ', '.RAW, .raw')

                     Supported element types:
                       binary data, signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit,
                       signed  32  bit, unsigned 32 bit, floating point 32 bit, floating point 64
                       bit

       tif       TIFF 2D-image input/output support

                     ('Recognized file extensions: ', '.TIF, .TIFF, .tif, .tiff')

                     Supported element types:
                       binary data, unsigned 8 bit, unsigned 16 bit, unsigned 32 bit

       vista     a 2dimage io plugin for vista images

                     ('Recognized file extensions: ', '.-, .V, .VISTA, .v, .vista')

                     Supported element types:
                       binary data, signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit,
                       signed  32  bit, unsigned 32 bit, floating point 32 bit, floating point 64
                       bit

PLUGINS: 2dimage/maskedcost

       lncc      local normalized cross correlation with masking support.,  supported  parameters
                 are:

                     w = 5; uint in [1, 256]
                       half  width  of  the  window  used  for  evaluating  the  localized  cross
                       correlation.

       mi        Spline parzen based mutual information with masking., supported parameters are:

                     cut = 0; float in [0, 40]
                       Percentage of pixels  to  cut  at  high  and  low  intensities  to  remove
                       outliers.

                     mbins = 64; uint in [1, 256]
                       Number of histogram bins used for the moving image.

                     mkernel = [bspline:d=3]; factory
                       Spline  kernel for moving image parzen hinstogram.  For supported plug-ins
                       see PLUGINS:1d/splinekernel

                     rbins = 64; uint in [1, 256]
                       Number of histogram bins used for the reference image.

                     rkernel = [bspline:d=0]; factory
                       Spline kernel for reference image parzen hinstogram.  For supported  plug-
                       ins see PLUGINS:1d/splinekernel

       ncc       normalized cross correlation with masking support.

                     (no parameters)

       ssd       Sum of squared differences with masking.

                     (no parameters)

PLUGINS: 2dimage/transform

       affine    Affine transformation (six degrees of freedom)., supported parameters are:

                     imgboundary = mirror; factory
                       image  interpolation  boundary  conditions.   For  supported  plug-ins see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image    interpolator    kernel.     For    supported     plug-ins     see
                       PLUGINS:1d/splinekernel

       rigid     Rigid   transformations   (i.e.  rotation  and  translation,  three  degrees  of
                 freedom)., supported parameters are:

                     imgboundary = mirror; factory
                       image interpolation  boundary  conditions.   For  supported  plug-ins  see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image     interpolator     kernel.     For    supported    plug-ins    see
                       PLUGINS:1d/splinekernel

                     rot-center = [[0,0]]; 2dfvector
                       Relative rotation center, i.e.  <0.5,0.5> corresponds to the center of the
                       support rectangle.

       rotation  Rotation  transformations  (i.e.  rotation  about  a given center, one degree of
                 freedom)., supported parameters are:

                     imgboundary = mirror; factory
                       image interpolation  boundary  conditions.   For  supported  plug-ins  see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image     interpolator     kernel.     For    supported    plug-ins    see
                       PLUGINS:1d/splinekernel

                     rot-center = [[0,0]]; 2dfvector
                       Relative rotation center, i.e.  <0.5,0.5> corresponds to the center of the
                       support rectangle.

       spline    Free-form transformation that can be described by a set of B-spline coefficients
                 and an underlying B-spline kernel., supported parameters are:

                     anisorate = [[0,0]]; 2dfvector
                       anisotropic  coefficient  rate  in  pixels,  nonpositive  values  will  be
                       overwritten by the 'rate' value..

                     imgboundary = mirror; factory
                       image  interpolation  boundary  conditions.   For  supported  plug-ins see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image    interpolator    kernel.     For    supported     plug-ins     see
                       PLUGINS:1d/splinekernel

                     kernel = [bspline:d=3]; factory
                       transformation    spline    kernel..     For    supported   plug-ins   see
                       PLUGINS:1d/splinekernel

                     penalty = ; factory
                       Transformation    penalty    term.     For    supported    plug-ins    see
                       PLUGINS:2dtransform/splinepenalty

                     rate = 10; float in [1, inf)
                       isotropic coefficient rate in pixels.

       translate Translation only (two degrees of freedom), supported parameters are:

                     imgboundary = mirror; factory
                       image  interpolation  boundary  conditions.   For  supported  plug-ins see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image    interpolator    kernel.     For    supported     plug-ins     see
                       PLUGINS:1d/splinekernel

       vf        This  plug-in  implements  a  transformation that defines a translation for each
                 point of  the  grid  defining  the  domain  of  the  transformation.,  supported
                 parameters are:

                     imgboundary = mirror; factory
                       image  interpolation  boundary  conditions.   For  supported  plug-ins see
                       PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image    interpolator    kernel.     For    supported     plug-ins     see
                       PLUGINS:1d/splinekernel

PLUGINS: 2dtransform/splinepenalty

       divcurl   divcurl penalty on the transformation, supported parameters are:

                     curl = 1; float in [0, inf)
                       penalty weight on curl.

                     div = 1; float in [0, inf)
                       penalty weight on divergence.

                     norm = 0; bool
                       Set  to  1  if  the penalty should be normalized with respect to the image
                       size.

                     weight = 1; float in (0, inf)
                       weight of penalty energy.

PLUGINS: fastica/implementation

       internal  This is the MIA implementation of the FastICA algorithm.

                     (no parameters)

       itpp      This is the IT++ implementation of the FastICA algorithm.

                     (no parameters)

PLUGINS: minimizer/singlecost

       gdas      Gradient descent with automatic step size correction., supported parameters are:

                     ftolr = 0; double in [0, inf)
                       Stop if the relative change of the criterion is below..

                     max-step = 2; double in (0, inf)
                       Maximal absolute step size.

                     maxiter = 200; uint in [1, inf)
                       Stopping criterion: the maximum number of iterations.

                     min-step = 0.1; double in (0, inf)
                       Minimal absolute step size.

                     xtola = 0.01; double in [0, inf)
                       Stop if the inf-norm of the change applied to x is below this value..

       gdsq      Gradient descent with quadratic step estimation, supported parameters are:

                     ftolr = 0; double in [0, inf)
                       Stop if the relative change of the criterion is below..

                     gtola = 0; double in [0, inf)
                       Stop if the inf-norm of the gradient is below this value..

                     maxiter = 100; uint in [1, inf)
                       Stopping criterion: the maximum number of iterations.

                     scale = 2; double in (1, inf)
                       Fallback fixed step size scaling.

                     step = 0.1; double in (0, inf)
                       Initial step size.

                     xtola = 0; double in [0, inf)
                       Stop if the inf-norm of x-update is below this value..

       gsl       optimizer plugin based on the multimin optimizers of the GNU Scientific  Library
                 (GSL) https://www.gnu.org/software/gsl/, supported parameters are:

                     eps = 0.01; double in (0, inf)
                       gradient  based  optimizers:  stop  when  |grad| < eps, simplex: stop when
                       simplex size < eps..

                     iter = 100; uint in [1, inf)
                       maximum number of iterations.

                     opt = gd; dict
                       Specific optimizer to be used..  Supported values are:
                           bfgs ‐ Broyden-Fletcher-Goldfarb-Shann
                           bfgs2 ‐ Broyden-Fletcher-Goldfarb-Shann (most efficient version)
                           cg-fr ‐ Flecher-Reeves conjugate gradient algorithm
                           gd ‐ Gradient descent.
                           simplex ‐ Simplex algorithm of Nelder and Mead
                           cg-pr ‐ Polak-Ribiere conjugate gradient algorithm

                     step = 0.001; double in (0, inf)
                       initial step size.

                     tol = 0.1; double in (0, inf)
                       some tolerance parameter.

       nlopt     Minimizer  algorithms  using  the  NLOPT  library,  for  a  description  of  the
                 optimizers                 please                 see                'http://ab-
                 initio.mit.edu/wiki/index.php/NLopt_Algorithms', supported parameters are:

                     ftola = 0; double in [0, inf)
                       Stopping criterion: the absolute change of the objective  value  is  below
                       this value.

                     ftolr = 0; double in [0, inf)
                       Stopping  criterion:  the  relative change of the objective value is below
                       this value.

                     higher = inf; double
                       Higher boundary (equal for all parameters).

                     local-opt = none; dict
                       local  minimization  algorithm  that  may  be  required   for   the   main
                       minimization algorithm..  Supported values are:
                           gn-orig-direct-l   ‐  Dividing  Rectangles  (original  implementation,
                           locally biased)
                           gn-direct-l-noscal ‐ Dividing Rectangles (unscaled, locally biased)
                           gn-isres ‐ Improved Stochastic Ranking Evolution Strategy
                           ld-tnewton ‐ Truncated Newton
                           gn-direct-l-rand ‐ Dividing Rectangles (locally biased, randomized)
                           ln-newuoa ‐ Derivative-free Unconstrained Optimization by  Iteratively
                           Constructed Quadratic Approximation
                           gn-direct-l-rand-noscale  ‐  Dividing  Rectangles  (unscaled,  locally
                           biased, randomized)
                           gn-orig-direct ‐ Dividing Rectangles (original implementation)
                           ld-tnewton-precond ‐ Preconditioned Truncated Newton
                           ld-tnewton-restart ‐ Truncated Newton with steepest-descent restarting
                           gn-direct ‐ Dividing Rectangles
                           ln-neldermead ‐ Nelder-Mead simplex algorithm
                           ln-cobyla ‐ Constrained Optimization BY Linear Approximation
                           gn-crs2-lm ‐ Controlled Random Search with Local Mutation
                           ld-var2 ‐ Shifted Limited-Memory Variable-Metric, Rank 2
                           ld-var1 ‐ Shifted Limited-Memory Variable-Metric, Rank 1
                           ld-mma ‐ Method of Moving Asymptotes
                           ld-lbfgs-nocedal ‐ None
                           ld-lbfgs ‐ Low-storage BFGS
                           gn-direct-l ‐ Dividing Rectangles (locally biased)
                           none ‐ don't specify algorithm
                           ln-bobyqa ‐ Derivative-free Bound-constrained Optimization
                           ln-sbplx ‐ Subplex variant of Nelder-Mead
                           ln-newuoa-bound ‐ Derivative-free  Bound-constrained  Optimization  by
                           Iteratively Constructed Quadratic Approximation
                           ln-praxis  ‐  Gradient-free  Local Optimization via the Principal-Axis
                           Method
                           gn-direct-noscal ‐ Dividing Rectangles (unscaled)
                           ld-tnewton-precond-restart  ‐  Preconditioned  Truncated  Newton  with
                           steepest-descent restarting

                     lower = -inf; double
                       Lower boundary (equal for all parameters).

                     maxiter = 100; int in [1, inf)
                       Stopping criterion: the maximum number of iterations.

                     opt = ld-lbfgs; dict
                       main minimization algorithm.  Supported values are:
                           gn-orig-direct-l   ‐  Dividing  Rectangles  (original  implementation,
                           locally biased)
                           g-mlsl-lds  ‐  Multi-Level  Single-Linkage  (low-discrepancy-sequence,
                           require local gradient based optimization and bounds)
                           gn-direct-l-noscal ‐ Dividing Rectangles (unscaled, locally biased)
                           gn-isres ‐ Improved Stochastic Ranking Evolution Strategy
                           ld-tnewton ‐ Truncated Newton
                           gn-direct-l-rand ‐ Dividing Rectangles (locally biased, randomized)
                           ln-newuoa  ‐ Derivative-free Unconstrained Optimization by Iteratively
                           Constructed Quadratic Approximation
                           gn-direct-l-rand-noscale  ‐  Dividing  Rectangles  (unscaled,  locally
                           biased, randomized)
                           gn-orig-direct ‐ Dividing Rectangles (original implementation)
                           ld-tnewton-precond ‐ Preconditioned Truncated Newton
                           ld-tnewton-restart ‐ Truncated Newton with steepest-descent restarting
                           gn-direct ‐ Dividing Rectangles
                           auglag-eq  ‐  Augmented Lagrangian algorithm with equality constraints
                           only
                           ln-neldermead ‐ Nelder-Mead simplex algorithm
                           ln-cobyla ‐ Constrained Optimization BY Linear Approximation
                           gn-crs2-lm ‐ Controlled Random Search with Local Mutation
                           ld-var2 ‐ Shifted Limited-Memory Variable-Metric, Rank 2
                           ld-var1 ‐ Shifted Limited-Memory Variable-Metric, Rank 1
                           ld-mma ‐ Method of Moving Asymptotes
                           ld-lbfgs-nocedal ‐ None
                           g-mlsl ‐ Multi-Level Single-Linkage (require  local  optimization  and
                           bounds)
                           ld-lbfgs ‐ Low-storage BFGS
                           gn-direct-l ‐ Dividing Rectangles (locally biased)
                           ln-bobyqa ‐ Derivative-free Bound-constrained Optimization
                           ln-sbplx ‐ Subplex variant of Nelder-Mead
                           ln-newuoa-bound  ‐  Derivative-free  Bound-constrained Optimization by
                           Iteratively Constructed Quadratic Approximation
                           auglag ‐ Augmented Lagrangian algorithm
                           ln-praxis ‐ Gradient-free Local Optimization  via  the  Principal-Axis
                           Method
                           gn-direct-noscal ‐ Dividing Rectangles (unscaled)
                           ld-tnewton-precond-restart  ‐  Preconditioned  Truncated  Newton  with
                           steepest-descent restarting
                           ld-slsqp ‐ Sequential Least-Squares Quadratic Programming

                     step = 0; double in [0, inf)
                       Initial step size for gradient free methods.

                     stop = -inf; double
                       Stopping criterion: function value falls below this value.

                     xtola = 0; double in [0, inf)
                       Stopping criterion: the absolute change of all  x-values  is  below   this
                       value.

                     xtolr = 0; double in [0, inf)
                       Stopping  criterion:  the  relative  change of all x-values is below  this
                       value.

EXAMPLE

       Register the perfusion series given in 'segment.set' by using  automatic  ICA  estimation.
       Skip  two  images  at  the  beginning and otherwiese use the default parameters. Store the
       result in 'registered.set'.

       mia-2dmyoica-full   -i segment.set -o registered.set -k 2

AUTHOR(s)

       Gert Wollny

COPYRIGHT

       This software is Copyright (c) 1999‐2015 Leipzig, Germany and  Madrid,  Spain.   It  comes
       with   ABSOLUTELY   NO  WARRANTY  and  you  may redistribute it under the terms of the GNU
       GENERAL PUBLIC LICENSE Version 3 (or later). For more information run the program with the
       option '--copyright'.