Provided by: mia-tools_2.4.7-13_amd64 bug

NAME

       mia-2dmyomilles - Run a registration of a series of 2D images.

SYNOPSIS

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

DESCRIPTION

       mia-2dmyomilles  This  program  is  use  to  run  a  modified  version  of  the  ICA based
       registration approach described in

              Milles et al. 'Fully Automated Motion Correction in First-Pass Myocardial Perfusion
              MR   Image   Sequences',   Trans.   Med.   Imaging.,   27(11),   1611-1621,   2008.
              ⟨https://doi.org/10.1109/TMI.2008.928918⟩

        Changes include the  extraction  of  the  quasi-periodic  movement  in  free  breathingly
       acquired  data  sets  and  the  option  to run affine or rigid registration instead of the
       optimization of translations only.

OPTIONS

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

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

              -r --registered=
                     file name base for registered files

                 --save-references=
                     save synthetic reference images to this file base

                 --save-cropped=
                     save cropped image set to this file

                 --save-feature=
                     save the features images resulting from the ICA and some intermediate images
                     used  for the RV-LV segmentation with the given file name base to PNG files.
                     Also save the coefficients of the initial  best  and  the  final  IC  mixing
                     matrix.

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

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

              -g --guess
                     use initial guess for myocardial perfusion

              -s --segscale=1.4
                     segment and scale the crop box around the LV (0=no segmentation)

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

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

              -E --segmethod=features
                     Segmentation method

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

   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
              -c --cost=ssd
                     registration criterion

              -O --optimizer=gsl:opt=simplex,step=1.0
                     Optimizer used for minimization
                      For supported plugins see PLUGINS:minimizer/singlecost

              -f --transForm=rigid
                     transformation type
                      For supported plugins see PLUGINS:2dimage/transform

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

              -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

              -P --passes=2
                     registration passes

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/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:
                           simplex ‐ Simplex algorithm of Nelder and Mead
                           cg-fr ‐ Flecher-Reeves conjugate gradient algorithm
                           cg-pr ‐ Polak-Ribiere conjugate gradient algorithm
                           bfgs ‐ Broyden-Fletcher-Goldfarb-Shann
                           bfgs2 ‐ Broyden-Fletcher-Goldfarb-Shann (most efficient version)
                           gd ‐ Gradient descent.

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

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