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 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=(input,required)

input perfusion data set

-o --out-file=(output,required)

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

crorrelation threshhold 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:

info \(hy Low level messages

trace \(hy Function call trace

fail \(hy Report test failures

warning \(hy Warnings

error \(hy Report errors

debug \(hy Debug output

message \(hy Normal messages

fatal \(hy 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)

Stop if the relative change of the criterion is below.. in [0, INF]

max-step = 2 (double)

Minimal absolute step size. in [1, INF]

maxiter = 200 (uint)

Stopping criterion: the maximum number of iterations. in [1, 2147483647]

min-step = 0.1 (double)

Maximal absolute step size. in [1e-10, INF]

xtola = 0.01 (double)

Stop if the inf-norm of the change applied to x is below this value.. in [0, INF]

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 \(hy Broyden-Fletcher-Goldfarb-Shann

bfgs2 \(hy Broyden-Fletcher-Goldfarb-Shann (most efficient version)

cg-fr \(hy Flecher-Reeves conjugate gradient algorithm

gd \(hy Gradient descent.

simplex \(hy Simplex algorithm of Nelder and Mead

cg-pr \(hy 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 \(hy Dividing Rectangles (original implementation, locally biased)

gn-direct-l-noscal \(hy Dividing Rectangles (unscaled, locally biased)

gn-isres \(hy Improved Stochastic Ranking Evolution Strategy

ld-tnewton \(hy Truncated Newton

gn-direct-l-rand \(hy Dividing Rectangles (locally biased, randomized)

ln-newuoa \(hy Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation

gn-direct-l-rand-noscale \(hy Dividing Rectangles (unscaled, locally biased, randomized)

gn-orig-direct \(hy Dividing Rectangles (original implementation)

ld-tnewton-precond \(hy Preconditioned Truncated Newton

ld-tnewton-restart \(hy Truncated Newton with steepest-descent restarting

gn-direct \(hy Dividing Rectangles

ln-neldermead \(hy Nelder-Mead simplex algorithm

ln-cobyla \(hy Constrained Optimization BY Linear Approximation

gn-crs2-lm \(hy Controlled Random Search with Local Mutation

ld-var2 \(hy Shifted Limited-Memory Variable-Metric, Rank 2

ld-var1 \(hy Shifted Limited-Memory Variable-Metric, Rank 1

ld-mma \(hy Method of Moving Asymptotes

ld-lbfgs-nocedal \(hy None

ld-lbfgs \(hy Low-storage BFGS

gn-direct-l \(hy Dividing Rectangles (locally biased)

none \(hy don't specify algorithm

ln-bobyqa \(hy Derivative-free Bound-constrained Optimization

ln-sbplx \(hy Subplex variant of Nelder-Mead

ln-newuoa-bound \(hy Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation

ln-praxis \(hy Gradient-free Local Optimization via the Principal-Axis Method

gn-direct-noscal \(hy Dividing Rectangles (unscaled)

ld-tnewton-precond-restart \(hy 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 \(hy Dividing Rectangles (original implementation, locally biased)

g-mlsl-lds \(hy Multi-Level Single-Linkage (low-discrepancy-sequence, require local gradient based optimization and bounds)

gn-direct-l-noscal \(hy Dividing Rectangles (unscaled, locally biased)

gn-isres \(hy Improved Stochastic Ranking Evolution Strategy

ld-tnewton \(hy Truncated Newton

gn-direct-l-rand \(hy Dividing Rectangles (locally biased, randomized)

ln-newuoa \(hy Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation

gn-direct-l-rand-noscale \(hy Dividing Rectangles (unscaled, locally biased, randomized)

gn-orig-direct \(hy Dividing Rectangles (original implementation)

ld-tnewton-precond \(hy Preconditioned Truncated Newton

ld-tnewton-restart \(hy Truncated Newton with steepest-descent restarting

gn-direct \(hy Dividing Rectangles

auglag-eq \(hy Augmented Lagrangian algorithm with equality constraints only

ln-neldermead \(hy Nelder-Mead simplex algorithm

ln-cobyla \(hy Constrained Optimization BY Linear Approximation

gn-crs2-lm \(hy Controlled Random Search with Local Mutation

ld-var2 \(hy Shifted Limited-Memory Variable-Metric, Rank 2

ld-var1 \(hy Shifted Limited-Memory Variable-Metric, Rank 1

ld-mma \(hy Method of Moving Asymptotes

ld-lbfgs-nocedal \(hy None

g-mlsl \(hy Multi-Level Single-Linkage (require local optimization and bounds)

ld-lbfgs \(hy Low-storage BFGS

gn-direct-l \(hy Dividing Rectangles (locally biased)

ln-bobyqa \(hy Derivative-free Bound-constrained Optimization

ln-sbplx \(hy Subplex variant of Nelder-Mead

ln-newuoa-bound \(hy Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation

auglag \(hy Augmented Lagrangian algorithm

ln-praxis \(hy Gradient-free Local Optimization via the Principal-Axis Method

gn-direct-noscal \(hy Dividing Rectangles (unscaled)

ld-tnewton-precond-restart \(hy Preconditioned Truncated Newton with steepest-descent restarting

ld-slsqp \(hy 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 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\(hy2013 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'.