Provided by: mia-tools_2.4.6-4ubuntu2_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=(input, required); string
                     input perfusion data set

              -o --out-file=(output, required); string
                     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=(output); string
                     save cropped set to this file, the image files will use the stem of the name
                     as file name base

                 --save-feature=(output); string
                     save segmentation feature images and initial ICA mixing matrix

                 --save-refs=(output); string
                     for each registration pass save the reference images to files with the given
                     name base

                 --save-regs=(output); string
                     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
                        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

   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

              -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

              -b --min-breathing-frequency=-1
                     minimal mean frequency a mixing curve can have to be considered to stem from
                     brething. A healthy rest breating rate is 12 per minute.  A  negative  value
                     disables the test.

   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 in [0, 5]
                       Spline degree.

       omoms     OMoms-spline kernel creation, supported parameters are:

                     d = 3; int in [3, 3]
                       Spline degree.

PLUGINS: 2dimage/cost

       lncc      local normalized cross correlation with masking support.,  supported  parameters
                 are:

                     w = 5; uint in [1, 256]
                       half  width  of  the  window  used  for  evaluating  the  localized  cross
                       correlation.

       lsd       Least-Squares Distance measure

                     (no parameters)

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

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

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

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

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

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

       ncc       normalized cross correlation.

                     (no parameters)

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

                     eval = ds; dict
                       plugin subtype.  Supported values are:
                           sq ‐ square of difference
                           ds ‐ square of scaled difference
                           dot ‐ scalar product kernel
                           cross ‐ cross product kernel

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

                     autothresh = 0; float in [0, 1000]
                       Use automatic masking of the moving image by only takeing intensity values
                       into accound that are larger than the given threshold.

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

       ssd-automask
                 2D  image  cost:  sum  of  squared  differences, with automasking based on given
                 thresholds, supported parameters are:

                     rthresh = 0; double
                       Threshold intensity value for reference image.

                     sthresh = 0; double
                       Threshold intensity value for source image.

PLUGINS: 2dimage/fullcost

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

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

                     weight = 1; float
                       weight of cost function.

       labelimage
                 Similarity cost function that maps labels  of  two  images  and  handles  label-
                 preserving multi-resolution processing., supported parameters are:

                     debug = 0; int in [0, 1]
                       write the distance transforms to a 3D image.

                     maxlabel = 256; int in [2, 32000]
                       maximum number of labels to consider.

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

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

                     weight = 1; float
                       weight of cost function.

       maskedimage
                 Generalized  masked  image  similarity  cost  function  that also handles multi-
                 resolution processing. The provided masks should be  densly  filled  regions  in
                 multi-resolution  procesing  because otherwise the mask information may get lost
                 when downscaling the image. The reference mask and the transformed mask  of  the
                 study  image  are combined by binary AND. 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/maskedcost

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

                     ref-mask =(input, io)
                       Reference   image   mask    (binary).    For   supported  file  types  see
                       PLUGINS:2dimage/io

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

                     src-mask =(input, io)
                       Study   image   mask   (binary).    For   supported   file    types    see
                       PLUGINS:2dimage/io

                     weight = 1; float
                       weight of cost function.

PLUGINS: 2dimage/io

       bmp       BMP  2D-image  input/output support. The plug-in supports reading and writing of
                 binary images and 8-bit gray scale images. read-only  support  is  provided  for
                 4-bit  gray  scale  images.  The color table is ignored and the pixel values are
                 taken as literal gray scale values.

                     ('Recognized file extensions: ', '.BMP, .bmp')

                     Supported element types:
                       binary data, unsigned 8 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:
                       signed 16 bit, 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: 2dimage/maskedcost

       lncc      local normalized cross correlation with masking support.,  supported  parameters
                 are:

                     w = 5; uint in [1, 256]
                       half  width  of  the  window  used  for  evaluating  the  localized  cross
                       correlation.

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

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

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

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

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

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

       ncc       normalized cross correlation with masking support.

                     (no parameters)

       ssd       Sum of squared differences with masking.

                     (no parameters)

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:
                           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-nonrigid-parallel   -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'.