xenial (1) mia-2dmyoperiodic-nonrigid.1.gz

Provided by: mia-tools_2.2.7-3_amd64 bug

NAME

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

SYNOPSIS

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

DESCRIPTION

       mia-2dmyoperiodic-nonrigid  This  program  runs  the  non-rigid registration of an perfusion image series
       preferable acquired letting the patient breath freely. The registration algorithm implementes G.  Wollny,
       M-J  Ledesma-Cabryo,  P.Kellman, and A.Santos, "Exploiting Quasiperiodicity in Motion Correction of Free-
       Breathing," IEEE Transactions on Medical Imaging, 29(8), 2010

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-references
                     Save synthetic references to files refXXXX.v

   Preconditions
              -k --skip=0
                     Skip images at the begin of the seriesSkip images at the begin of the series

                 --max-candidates=20
                     maximum number of candidates for global reference imagemaximum  number  of  candidates  for
                     global reference image

              -S --cost-series=image:cost=[ngf:eval=ds]
                     Const  function to use for the analysis of the seriesConst function to use for the analysis
                     of the series  For supported plugins see PLUGINS:2dimage/fullcost

                 --ref-idx=
                     save reference index number to this file

              -R --global-reference=-1
                     save reference index number to this filesave reference index number to this file

              -D --max-subset-delta=0
                     Maximum delta between two elements  of  the  prealigned  subsetMaximum  delta  between  two
                     elements of the prealigned subset

   Registration
              -O --optimizer=gsl:opt=gd,step=0.01
                     Optimizer  used  for minimizationOptimizer used for minimization  For supported plugins see
                     PLUGINS:minimizer/singlecost

                 --refiner=
                     optimizer used for additional minimizationoptimizer used for additional  minimization   For
                     supported plugins see PLUGINS:minimizer/singlecost

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

              -f --transForm=spline:rate=16,penalty=[divcurl:weight=0.01]
                     transformation typetransformation type  For supported plugins see PLUGINS:2dimage/transform

              -1 --cost-subset=image:cost=[ngf:eval=ds]
                     Cost function for registration during the subset registrationCost function for registration
                     during the subset registration  For supported plugins see PLUGINS:2dimage/fullcost

              -2 --cost-final=image:cost=ssd
                     Cost function for registration during the final registrationCost function for  registration
                     during the final registration  For supported plugins see PLUGINS:2dimage/fullcost

   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/splinebc

       mirror    Spline interpolation boundary conditions that mirror on the boundary

                     (no parameters)

       repeat    Spline interpolation boundary conditions that repeats the value at the boundary

                     (no parameters)

       zero      Spline interpolation boundary conditions that assumes zero for values outside

                     (no parameters)

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 availabe., 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, string)
                       Reference image.

                     src =(input, string)
                       Study image.

                     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, string)
                       Reference image.

                     src =(input, string)
                       Study image.

                     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, string)
                       Reference image.

                     ref-mask =(input, string)
                       Reference image mask  (binary).

                     src =(input, string)
                       Study image.

                     src-mask =(input, string)
                       Study image mask (binary).

                     weight = 1; float
                       weight of cost function.

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:
                       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: 2dimage/transform

       affine    Affine transformation (six degrees of freedom)., supported parameters are:

                     imgboundary = mirror; factory
                       image interpolation boundary conditions.  For supported plug-ins see PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image interpolator kernel.  For supported plug-ins see PLUGINS:1d/splinekernel

       rigid     Rigid transformations (i.e. rotation and translation, three  degrees  of  freedom).,  supported
                 parameters are:

                     imgboundary = mirror; factory
                       image interpolation boundary conditions.  For supported plug-ins see PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image interpolator kernel.  For supported plug-ins see PLUGINS:1d/splinekernel

                     rot-center = [[0,0]]; 2dfvector
                       Relative  rotation  center,  i.e.   <0.5,0.5>  corresponds  to  the center of the support
                       rectangle.

       rotation  Rotation transformations (i.e.  rotation  about  a  given  center,  one  degree  of  freedom).,
                 supported parameters are:

                     imgboundary = mirror; factory
                       image interpolation boundary conditions.  For supported plug-ins see PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image interpolator kernel.  For supported plug-ins see PLUGINS:1d/splinekernel

                     rot-center = [[0,0]]; 2dfvector
                       Relative  rotation  center,  i.e.   <0.5,0.5>  corresponds  to  the center of the support
                       rectangle.

       spline    Free-form transformation that can be described  by  a  set  of  B-spline  coefficients  and  an
                 underlying B-spline kernel., supported parameters are:

                     anisorate = [[0,0]]; 2dfvector
                       anisotropic  coefficient  rate  in  pixels, nonpositive values will be overwritten by the
                       'rate' value..

                     imgboundary = mirror; factory
                       image interpolation boundary conditions.  For supported plug-ins see PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image interpolator kernel.  For supported plug-ins see PLUGINS:1d/splinekernel

                     kernel = [bspline:d=3]; factory
                       transformation spline kernel..  For supported plug-ins see PLUGINS:1d/splinekernel

                     penalty = ; factory
                       Transformation      penalty      term.       For       supported       plug-ins       see
                       PLUGINS:2dtransform/splinepenalty

                     rate = 10; float in [1, inf)
                       isotropic coefficient rate in pixels.

       translate Translation only (two degrees of freedom), supported parameters are:

                     imgboundary = mirror; factory
                       image interpolation boundary conditions.  For supported plug-ins see PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image interpolator kernel.  For supported plug-ins see PLUGINS:1d/splinekernel

       vf        This  plug-in implements a transformation that defines a translation for each point of the grid
                 defining the domain of the transformation., supported parameters are:

                     imgboundary = mirror; factory
                       image interpolation boundary conditions.  For supported plug-ins see PLUGINS:1d/splinebc

                     imgkernel = [bspline:d=3]; factory
                       image interpolator kernel.  For supported plug-ins see PLUGINS:1d/splinekernel

PLUGINS: 2dtransform/splinepenalty

       divcurl   divcurl penalty on the transformation, supported parameters are:

                     curl = 1; float in [0, inf)
                       penalty weight on curl.

                     div = 1; float in [0, inf)
                       penalty weight on divergence.

                     norm = 0; bool
                       Set to 1 if the penalty should be normalized with respect to the image size.

                     weight = 1; float in (0, inf)
                       weight of penalty energy.

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'.  Skip two images at the beginning, usa spline
       transformation of a knot rate 16 pixels, and penalize the transformation by divcurl with weight 5.  Store
       the result in 'registered.set'.

       mia-2dmyoperiodic-nonrigid       -i     segment.set     -o     registered.set    -k    2    -d    5    -f
              spline:rate=16,penalty=[divcurl:weight=5]

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'.