Provided by: mia-tools_2.0.13-1_amd64 bug

NAME

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

SYNOPSIS

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

DESCRIPTION

       mia-2dmyoica-nonrigid-parallel  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.This  version  of the program may run all registrations in
       parallel.

OPTIONS

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

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

              -r --registered=reg
                     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=
                     save cropped set to this file, the image files will use the stem of the name
                     as file name base

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

                 --save-refs=
                     for each registration pass save the reference images to files with the given
                     name base

                 --save-regs=
                     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
                        warning ‐ Warnings
                        error ‐ Report errors
                        fail ‐ Report test failures
                        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
              -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

   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
              -O --optimizer=gsl:opt=gd,step=0.1
                     Optimizer  used for minimization. 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.

              -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 --passes=3
                     registration passes

PLUGINS: 1d/splinekernel

       bspline   B-spline kernel creation , supported parameters are:

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

       omoms     OMoms-spline kernel creation, supported parameters are:

                     d = 3 (int)
                       Spline degree.  in [3, 3]

PLUGINS: 2dimage/cost

       lsd       Least-Squares Distance measure

                     (no parameters)

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

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

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

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

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

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

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

                     eval = ds (string)
                       plugin subtype (sq, ds,dot,cross).

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

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

PLUGINS: 2dimage/fullcost

       divcurl   divcurl penalty cost function, supported parameters are:

                     curl = 1 (float)
                       penalty weight on curl.  in [0, 3.40282e+38]

                     div = 1 (float)
                       penalty weight on divergence.  in [0, 3.40282e+38]

                     weight = 1 (float)
                       weight of cost function.  in [-1e+10, 1e+10]

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

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

                     weight = 1 (float)
                       weight of cost function.  in [-1e+10, 1e+10]

PLUGINS: 2dimage/io

       bmp       BMP 2D-image input/output support

                     Recognized file extensions:  .BMP, .bmp

                     Supported element types:
                       binary data, unsigned 8 bit, unsigned 16 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:
                       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: minimizer/singlecost

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

                     ftolr = 0 (double)
                       Stop if the relative change of the criterion is below..  in [0, INF]

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

                     maxiter = 100 (uint)
                       Stopping criterion: the maximum number of iterations.  in [1, 2147483647]

                     scale = 2 (double)
                       Fallback fixed step size scaling.  in [1, INF]

                     step = 0.1 (double)
                       Initial step size.  in [0, INF]

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

       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)
                       gradient based optimizers: stop when |grad|  <  eps,  simplex:  stop  when
                       simplex size < eps..  in [1e-10, 10]

                     iter = 100 (int)
                       maximum number of iterations.  in [1, 2147483647]

                     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)
                       initial step size.  in [0, 10]

                     tol = 0.1 (double)
                       some tolerance parameter.  in [0.001, 10]

       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)
                       Stopping  criterion:  the  absolute change of the objective value is below
                       this value.  in [0, INF]

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

                     higher = inf (double)
                       Higher boundary (equal for all parameters).  in [INF, INF]

                     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).  in [INF, INF]

                     maxiter = 100 (int)
                       Stopping criterion: the maximum number of iterations.  in [1, 2147483647]

                     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)
                       Initial step size for gradient free methods.  in [0, INF]

                     stop = -inf (double)
                       Stopping criterion: function value falls below this value.  in [INF, INF]

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

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

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-nonrigid-parallel   -i segment.set -o registered.set -k 2

AUTHOR(s)

       Gert Wollny

COPYRIGHT

       This  software  is  Copyright  (c) 1999‐2013 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'.