Provided by: mia-tools_2.4.7-8ubuntu1_amd64 bug

NAME

       mia-2dmyopgt-nonrigid - Run a registration of a series of 2D images.

SYNOPSIS

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

DESCRIPTION

       mia-2dmyopgt-nonrigid  This program implements the non-linear registration based on Pseudo
       Ground Thruth for motion compensation of series of myocardial perfusion images given as  a
       data set as described in

              Chao  Li  and  Ying  Sun,  'Nonrigid Registration of Myocardial Perfusion MRI Using
              Pseudo Ground Truth' , In  Proc.  Medical  Image  Computing  and  Computer-Assisted
              Intervention            MICCAI            2009,            165-172,            2009
              ⟨https://doi.org/10.1007/978-3-642-04268-3_21⟩

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 files, the image file  type  is  the  same  as
                     given in the input data set

   Pseudo Ground Thruth estimation
              -A --alpha=1
                     spacial neighborhood penalty weight

              -B --beta=1
                     temporal second derivative penalty weight

              -R --rho-thresh=0.85
                     correlation threshold for neighborhood analysis

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

   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

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

              -P --passes=4
                     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: 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  Pseudo  Ground  Truth
       estimation.  Skip  two  images at the beginning and otherwiese use the default parameters.
       Store the result in 'registered.set'.

       mia-2dmyopgt-nonrigid -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'.