Provided by: mia-tools_2.4.6-5build3_amd64 bug

NAME

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

SYNOPSIS

       mia-2dmyoserial-nonrigid -i <in-file> -o <out-file> [options] <PLUGINS:2dimage/fullcost>

DESCRIPTION

       mia-2dmyoserial-nonrigid This program runs the non-rigid motion compensation registration of an perfusion
       image series. The registration is run in a serial manner, this is, only images in temporal succession are
       registered, and the obtained transformations are applied accumulated to reach full registration. See e.g.
       Wollny, G., Ledesma-Carbayo, M.J., Kellman, P., Santos,  A.  "A  New  Similarity  Measure  for  Non-Rigid
       Breathing  Motion  Compensation  of  Myocardial Perfusion MRI ". Proc 30th Annual International IEEE EMBS
       Conference, pp. 3389-3392. Vancouver, Aug. 2008, doi:10.1109/IEMBS.2008.4649933,

OPTIONS

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

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

              -R --registered=reg
                     file name base for registered fiels

   Registration
              -O --optimizer=gsl:opt=gd,step=0.1
                     Optimizer used for minimization
                      For supported plugins see PLUGINS:minimizer/singlecost

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

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

              -r --ref=-1
                     reference frame (-1 == use image in the middle)

              -k --skip=0
                     skip registration of these images at the beginning of the series

   Help & Info
              -V --verbose=warning
                     verbosity of output, print  messages  of  given  level  and  higher  priorities.  Supported
                     priorities starting at lowest level are:

                        trace ‐ Function call trace
                        debug ‐ Debug output
                        info ‐ Low level messages
                        message ‐ Normal messages
                        warning ‐ Warnings
                        fail ‐ Report test failures
                        error ‐ Report errors
                        fatal ‐ Report only fatal errors

                 --copyright
                     print copyright information

              -h --help
                     print this help

              -? --usage
                     print a short help

                 --version
                     print the version number and exit

   Processing
                 --threads=-1
                     Maxiumum  number  of  threads to use for processing,This number should be lower or equal to
                     the number of logical processor cores in the machine. (-1: automatic estimation).

PLUGINS: 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 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: 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  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' to reference  image  30.  Skip  two  images  at  the
       beginning  and using mutual information as cost function, and penalize the transformation by divcurl with
       weight 5. Store the result in 'registered.set'.

       mia-2dmyoserial-nonrigid    -i  segment.set  -o   registered.set   -k   2    -r   30   image:cost=mi   -f
              spline:rate=5,penalty=[divcurl:weight=5]

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'.