xenial (1) mia-2dmyoica-nonrigid2.1.gz

Provided by: mia-tools_2.2.7-3_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=(input, required); 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
              -C --components=0
                     ICA components 0 = automatic estimationICA 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)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 modalitiesskip
                     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 ICAmaximum 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

   Registration
              -O --optimizer=gsl:opt=gd,step=0.1
                     Optimizer  used  for minimizationOptimizer 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  passstart
                     coefficinet rate in spines, gets divided by --c-rate-divider with every pass

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

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

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

              -w --imageweight=1
                     image cost weightimage cost weight

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

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

              -P --passes=3
                     registration passesregistration passes

   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

   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).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: 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  ofthe  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-nonrigid2   -i segment.set -o registered.set -k 2

AUTHOR(s)

       Gert Wollny

       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'.