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

NAME

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

SYNOPSIS

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

DESCRIPTION

       mia-2dmyoica-nonrigid  This program implements 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.
              ⟨https://doi.org/10.1016/j.media.2012.02.004⟩

OPTIONS

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

              -o --out-file=(required, output); 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 the features images resulting from the ICA and some intermediate images
                     used for the RV-LV segmentation with the given file name base to PNG  files.
                     Also  save  the  coefficients  of  the  initial best and the final IC mixing
                     matrix.

                 --save-refs=
                     save synthetic reference images

                 --save-regs=
                     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:

                        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

   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-feature ‐ difference of the feature images
                        delta-peak ‐ difference of the peak enhancement images
                        features ‐ 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
                      For supported plugins see PLUGINS:minimizer/singlecost

              -R --refiner=
                     optimizer used for refinement after the main optimizer was called
                      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=10
                     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
                      For supported plugins see PLUGINS:2dimage/fullcost

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

              -P --passes=5
                     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:
                           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-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'.