Provided by: mia-tools_2.4.6-5build3_amd64 
NAME
mia-2dmyoica-nonrigid2 - Run a registration of a series of 2D images.
SYNOPSIS
mia-2dmyoica-nonrigid2 -i <in-file> -o <out-file> [options]
DESCRIPTION
mia-2dmyoica-nonrigid2 This program runs the non-rigid registration of an perfusion image
series.In each pass, first an ICA analysis is run to estimate and eliminate the periodic
movement and create reference images with intensities similar to the corresponding
original image. Then non-rigid registration is run using the an "ssd + divcurl" cost
model. The B-spline c-rate and the divcurl cost weight are changed in each pass according
to given parameters.In the first pass a bounding box around the LV myocardium may be
extractedto speed up computation Special note to this implemnentation: the registration is
always run from the original images to avoid the accumulation of interpolation errors.
OPTIONS
File-IO
-i --in-file=(required, input); string
input perfusion data set
-o --out-file=(output, required); string
output perfusion data set
-r --registered=reg
file name base for registered fiels
--save-cropped=
save cropped set to this file
--save-feature=
save segmentation feature images and initial ICA mixing matrix
ICA
--fastica=internal
FastICA implementationto be used
For supported plugins see PLUGINS:fastica/implementation
-C --components=0
ICA components 0 = automatic estimation
--normalize
don't normalized ICs
--no-meanstrip
don't strip the mean from the mixing curves
-s --segscale=0
segment and scale the crop box around the LV (0=no segmentation)
-k --skip=0
skip images at the beginning of the series e.g. because as they are of other
modalities
-m --max-ica-iter=400
maximum number of iterations in ICA
-E --segmethod=features
Segmentation method
delta-feature ‐ difference of the feature images
delta-peak ‐ difference of the peak enhancement images
features ‐ feature images
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
-p --interpolator=bspline:d=3
image interpolator kernel
For supported plugins see PLUGINS:1d/splinekernel
-l --mg-levels=3
multi-resolution levels
-P --passes=3
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: 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: fastica/implementation
internal This is the MIA implementation of the FastICA algorithm.
(no parameters)
itpp This is the IT++ implementation of the FastICA algorithm.
(no parameters)
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 automatic ICA estimation.
Skip two images at the beginning and otherwiese use the default parameters. Store the
result in 'registered.set'.
mia-2dmyoica-nonrigid2 -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'.