--- name: "mia-2dmyopgt-nonrigid" section: "1" platform: "linux" package: "mia-tools" version: "2.4.7-11+b1" release: "Debian 12 (Bookworm)" platforms: ["linux"] url: "https://new.carta.tech/mia-2dmyopgt-nonrigid" --- # mia-2dmyopgt-nonrigid(1) Run a registration of a series of 2D images. ## NAME mia-2dmyopgt-nonrigid - Run a registration of a series of 2D images. ## SYNOPSIS **mia-2dmyopgt-nonrigid -i \ -o \ \[options\]** ## DESCRIPTION **mia-2dmyopgt-nonrigid** This program implements the non-linear registration based on Pseudo Ground Thruth for motion compensation of series of myocardial perfusion images given as a data set 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⟩ ## 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 files, the image file type is the same as given in the input data set ### Pseudo Ground Thruth estimation - \-A --alpha=1 spacial neighborhood penalty weight - \-B --beta=1 temporal second derivative penalty weight - \-R --rho-thresh=0.85 correlation threshold for neighborhood analysis - \-k --skip=0 skip images at the beginning of the series e.g. because as they are of other modalities ### Registration - \-O --optimizer=gsl:opt=gd,step=0.1 Optimizer used for minimization\ For supported plugins see PLUGINS:minimizer/singlecost - \-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 - \-d --start-divcurl=20 start divcurl weight, gets divided by --divcurl-divider with every pass - \--divcurl-divider=4 divcurl weight scaling with each new pass - \-w --imageweight=1 image cost weight - \-l --mg-levels=3 multi-resolution levels - \-P --passes=4 registration passes ### 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: 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 Pseudo Ground Truth estimation. Skip two images at the beginning and otherwiese use the default parameters. Store the result in 'registered.set'. mia-2dmyopgt-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'.