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

NAME

       mia-2dmyoica-nonrigid2 - Run a registration of a series of 2D images.

SYNOPSIS

       mia-2dmyoica-nonrigid2 -i <in-file> -o <out-file> [options]

DESCRIPTION

       mia-2dmyoica-nonrigid2  This program runs the non-rigid registration of an perfusion image
       series.In each pass, first an ICA analysis is run to estimate and eliminate  the  periodic
       movement  and  create  reference  images  with  intensities  similar  to the corresponding
       original image. Then non-rigid registration is run using  the  an  "ssd  +  divcurl"  cost
       model.  The B-spline c-rate and the divcurl cost weight are changed in each pass according
       to given parameters.In the first pass a bounding box  around  the  LV  myocardium  may  be
       extractedto speed up computation Special note to this implemnentation: the registration is
       always run from the original images to avoid the accumulation of interpolation errors.

OPTIONS

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

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

              -r --registered=reg
                     file name base for registered fiels

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

                 --save-feature=
                     save segmentation feature images and initial ICA mixing matrix

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

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

                 --normalize
                     don't 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-feature ‐ difference of the feature images
                        delta-peak ‐ difference of the peak enhancement images
                        features ‐ feature images

   Registration
              -O --optimizer=gsl:opt=gd,step=0.1
                     Optimizer used for minimization
                      For supported plugins see PLUGINS:minimizer/singlecost

              -a --start-c-rate=32
                     start  coefficinet  rate  in  spines,  gets divided by --c-rate-divider with
                     every pass

                 --c-rate-divider=4
                     cofficient rate divider for each pass

              -d --start-divcurl=20
                     start divcurl weight, gets divided by --divcurl-divider with every pass

                 --divcurl-divider=4
                     divcurl weight scaling with each new pass

              -w --imageweight=1
                     image cost weight

              -p --interpolator=bspline:d=3
                     image interpolator kernel
                      For supported plugins see PLUGINS:1d/splinekernel

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

              -P --passes=3
                     registration passes

   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

   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).

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: 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-2dmyoica-nonrigid2   -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'.