jammy (1) mia-2dgroundtruthreg.1.gz
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 described 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. ⟨https://doi.org/10.1007/978-3-642-04268-3_21⟩ Note that for this nonlinear motion correction a preceding linear registration step is usually required.
OPTIONS
File-IO -i --in-file=(input, required); string input perfusion data set -o --out-file=(required, output); string 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); double spacial neighborhood penalty weight -B --beta=(required); double temporal second derivative penalty weight -R --rho_thresh=(required); double correlation threshold 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: 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/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: 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 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) 1999‐2015 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'.