Provided by: mia-tools_2.0.13-1_amd64
NAME
mia-2dgroundtruthreg - Registration of a series of 2D images
SYNOPSIS
mia-2dgroundtruthreg -i <in-file> -o <out-file> -A <alpha> -B <beta> -R <rho_thresh> [options]
DESCRIPTION
mia-2dgroundtruthreg This program implements the non-linear registration based on Pseudo Ground Thruth for motion compensation of series of myocardial perfusion images as decribed in Chao Li and Ying Sun, 'Nonrigid Registration of Myocardial Perfusion MRI Using Pseudo Ground Truth' , In Proc. Medical Image Computing and Computer-Assisted Intervention MICCAI 2009, 165-172, 2009. Note that for this nonlinear motion correction a preceding linear registration step is usually required.
OPTIONS
File-IO -i --in-file=(required) input perfusion data set -o --out-file=(required) output perfusion data set -r --registered=reg file name base for registered files Preconditions -s --skip=2 skip images at beginning of series -P --passes=4 number of registration passes Pseudo-Ground-Thruth -A --alpha=(required) spacial neighborhood penalty weight -B --beta=(required) temporal second derivative penalty weight -R --rho_thresh=(required) crorrelation threshhold for neighborhood analysis Registration -O --optimizer=gsl:opt=gd,step=0.1 Optimizer used for minimization For supported plugins see PLUGINS:minimizer/singlecost -p --interpolator=bspline:d=3 image interpolator kernel For supported plugins see PLUGINS:1d/splinekernel -l --mr-levels=3 multi-resolution levels -d --divcurl=20 divcurl regularization weight --divcurl-divider=4 divcurl weight scaling with each new pass -a --start-c-rate=32 start coefficinet rate in spines, gets divided by --c-rate-divider with every pass --c-rate-divider=4 cofficient rate divider for each pass -w --imageweight=1 image cost weight Help & Info -V --verbose=warning verbosity of output, print messages of given level and higher priorities. Supported priorities starting at lowest level are: info ‐ Low level messages warning ‐ Warnings error ‐ Report errors fail ‐ Report test failures message ‐ Normal messages fatal ‐ Report only fatal errors --copyright print copyright information -h --help print this help -? --usage print a short help --version print the version number and exit 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/splinekernel
bspline B-spline kernel creation , supported parameters are: d = 3 (int) Spline degree. in [0, 5] omoms OMoms-spline kernel creation, supported parameters are: d = 3 (int) Spline degree. in [3, 3]
PLUGINS: minimizer/singlecost
gdsq Gradient descent with quadratic step estimation, supported parameters are: ftolr = 0 (double) Stop if the relative change of the criterion is below.. in [0, INF] gtola = 0 (double) Stop if the inf-norm of the gradient is below this value.. in [0, INF] maxiter = 100 (uint) Stopping criterion: the maximum number of iterations. in [1, 2147483647] scale = 2 (double) Fallback fixed step size scaling. in [1, INF] step = 0.1 (double) Initial step size. in [0, INF] xtola = 0 (double) Stop if the inf-norm of x-update is below this value.. in [0, INF] gsl optimizer plugin based on the multimin optimizers ofthe GNU Scientific Library (GSL) https://www.gnu.org/software/gsl/, supported parameters are: eps = 0.01 (double) gradient based optimizers: stop when |grad| < eps, simplex: stop when simplex size < eps.. in [1e-10, 10] iter = 100 (int) maximum number of iterations. in [1, 2147483647] opt = gd (dict) Specific optimizer to be used.. Supported values are: bfgs ‐ Broyden-Fletcher-Goldfarb-Shann bfgs2 ‐ Broyden-Fletcher-Goldfarb-Shann (most efficient version) cg-fr ‐ Flecher-Reeves conjugate gradient algorithm gd ‐ Gradient descent. simplex ‐ Simplex algorithm of Nelder and Mead cg-pr ‐ Polak-Ribiere conjugate gradient algorithm step = 0.001 (double) initial step size. in [0, 10] tol = 0.1 (double) some tolerance parameter. in [0.001, 10] nlopt Minimizer algorithms using the NLOPT library, for a description of the optimizers please see 'http://ab- initio.mit.edu/wiki/index.php/NLopt_Algorithms', supported parameters are: ftola = 0 (double) Stopping criterion: the absolute change of the objective value is below this value. in [0, INF] ftolr = 0 (double) Stopping criterion: the relative change of the objective value is below this value. in [0, INF] higher = inf (double) Higher boundary (equal for all parameters). in [INF, INF] local-opt = none (dict) local minimization algorithm that may be required for the main minimization algorithm.. Supported values are: gn-orig-direct-l ‐ Dividing Rectangles (original implementation, locally biased) gn-direct-l-noscal ‐ Dividing Rectangles (unscaled, locally biased) gn-isres ‐ Improved Stochastic Ranking Evolution Strategy ld-tnewton ‐ Truncated Newton gn-direct-l-rand ‐ Dividing Rectangles (locally biased, randomized) ln-newuoa ‐ Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation gn-direct-l-rand-noscale ‐ Dividing Rectangles (unscaled, locally biased, randomized) gn-orig-direct ‐ Dividing Rectangles (original implementation) ld-tnewton-precond ‐ Preconditioned Truncated Newton ld-tnewton-restart ‐ Truncated Newton with steepest-descent restarting gn-direct ‐ Dividing Rectangles ln-neldermead ‐ Nelder-Mead simplex algorithm ln-cobyla ‐ Constrained Optimization BY Linear Approximation gn-crs2-lm ‐ Controlled Random Search with Local Mutation ld-var2 ‐ Shifted Limited-Memory Variable-Metric, Rank 2 ld-var1 ‐ Shifted Limited-Memory Variable-Metric, Rank 1 ld-mma ‐ Method of Moving Asymptotes ld-lbfgs-nocedal ‐ None ld-lbfgs ‐ Low-storage BFGS gn-direct-l ‐ Dividing Rectangles (locally biased) none ‐ don't specify algorithm ln-bobyqa ‐ Derivative-free Bound-constrained Optimization ln-sbplx ‐ Subplex variant of Nelder-Mead ln-newuoa-bound ‐ Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation ln-praxis ‐ Gradient-free Local Optimization via the Principal-Axis Method gn-direct-noscal ‐ Dividing Rectangles (unscaled) ld-tnewton-precond-restart ‐ Preconditioned Truncated Newton with steepest-descent restarting lower = -inf (double) Lower boundary (equal for all parameters). in [INF, INF] maxiter = 100 (int) Stopping criterion: the maximum number of iterations. in [1, 2147483647] opt = ld-lbfgs (dict) main minimization algorithm. Supported values are: gn-orig-direct-l ‐ Dividing Rectangles (original implementation, locally biased) g-mlsl-lds ‐ Multi-Level Single-Linkage (low-discrepancy-sequence, require local gradient based optimization and bounds) gn-direct-l-noscal ‐ Dividing Rectangles (unscaled, locally biased) gn-isres ‐ Improved Stochastic Ranking Evolution Strategy ld-tnewton ‐ Truncated Newton gn-direct-l-rand ‐ Dividing Rectangles (locally biased, randomized) ln-newuoa ‐ Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation gn-direct-l-rand-noscale ‐ Dividing Rectangles (unscaled, locally biased, randomized) gn-orig-direct ‐ Dividing Rectangles (original implementation) ld-tnewton-precond ‐ Preconditioned Truncated Newton ld-tnewton-restart ‐ Truncated Newton with steepest-descent restarting gn-direct ‐ Dividing Rectangles auglag-eq ‐ Augmented Lagrangian algorithm with equality constraints only ln-neldermead ‐ Nelder-Mead simplex algorithm ln-cobyla ‐ Constrained Optimization BY Linear Approximation gn-crs2-lm ‐ Controlled Random Search with Local Mutation ld-var2 ‐ Shifted Limited-Memory Variable-Metric, Rank 2 ld-var1 ‐ Shifted Limited-Memory Variable-Metric, Rank 1 ld-mma ‐ Method of Moving Asymptotes ld-lbfgs-nocedal ‐ None g-mlsl ‐ Multi-Level Single-Linkage (require local optimization and bounds) ld-lbfgs ‐ Low-storage BFGS gn-direct-l ‐ Dividing Rectangles (locally biased) ln-bobyqa ‐ Derivative-free Bound-constrained Optimization ln-sbplx ‐ Subplex variant of Nelder-Mead ln-newuoa-bound ‐ Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation auglag ‐ Augmented Lagrangian algorithm ln-praxis ‐ Gradient-free Local Optimization via the Principal-Axis Method gn-direct-noscal ‐ Dividing Rectangles (unscaled) ld-tnewton-precond-restart ‐ Preconditioned Truncated Newton with steepest-descent restarting ld-slsqp ‐ Sequential Least-Squares Quadratic Programming step = 0 (double) Initial step size for gradient free methods. in [0, INF] stop = -inf (double) Stopping criterion: function value falls below this value. in [INF, INF] xtola = 0 (double) Stopping criterion: the absolute change of all x-values is below this value. in [0, INF] xtolr = 0 (double) Stopping criterion: the relative change of all x-values is below this value. in [0, INF]
EXAMPLE
Register the perfusion series given by images imageXXXX.exr by using Pseudo Ground Truth estimation. Skip two images at the beginning and otherwiese use the default parameters. Store the result images to 'regXXXX.exr'. mia-2dgroundtruthreg -i imageXXXX.exr -o regXXXX.exr -k 2
AUTHOR(s)
Gert Wollny
COPYRIGHT
This software is Copyright (c) 1999‐2013 Leipzig, Germany and Madrid, Spain. It comes with ABSOLUTELY NO WARRANTY and you may redistribute it under the terms of the GNU GENERAL PUBLIC LICENSE Version 3 (or later). For more information run the program with the option '--copyright'.