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mia-2dmyopgt-nonrigid(1) General Commands Manual mia-2dmyopgt-nonrigid(1)

NAME

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

SYNOPSIS

mia-2dmyopgt-nonrigid -i <in-file> -o <out-file> [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

OPTIONS

File-IO

input perfusion data set

output perfusion data set

file name base for registered files, the image file type is the same as given in the input data set

Pseudo Ground Thruth estimation

spacial neighborhood penalty weight

temporal second derivative penalty weight

correlation threshold for neighborhood analysis

skip images at the beginning of the series e.g. because as they are of other modalities

Registration

Optimizer used for minimization
For supported plugins see PLUGINS:minimizer/singlecost
start coefficinet rate in spines, gets divided by --c-rate-divider with every pass

cofficient rate divider for each pass

start divcurl weight, gets divided by --divcurl-divider with every pass

divcurl weight scaling with each new pass

image cost weight

multi-resolution levels

registration passes

Help & Info

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
print copyright information

print this help

-? --usage
print a short help

print the version number and exit

Processing

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

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..

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..

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.

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'.

v2.4.7 USER COMMANDS