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'.
2.0.10 25 January 2014 mia-2dgroundtruthreg(1)