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PROGRAM:

NAME


mia-2dmyomilles - Run a registration of a series of 2D images.

SYNOPSIS


mia-2dmyomilles -i <in-file> -o <out-file> [options]

DESCRIPTION


mia-2dmyomilles This program is use to run a modified version of the ICA based
registration approach described in Milles et al. 'Fully Automated Motion Correction in
First-Pass Myocardial Perfusion MR Image Sequences', Trans. Med. Imaging., 27(11),
1611-1621, 2008. Changes include the extraction of the quasi-periodic movement in free
breathingly acquired data sets and the option to run affine or rigid registration instead
of the optimization of translations only.

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=
file name base for registered files

--save-references=
save synthetic reference images to this file base

--save-cropped=
save cropped image set to this file

--save-feature=
save the features images resulting from the ICA and some intermediate images
used for the RV-LV segmentation with the given file name base to PNG files.
Also save the coefficients of the initial best and the final IC mixing
matrix.

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
trace ‐ Function call trace
fail ‐ Report test failures
warning ‐ Warnings
error ‐ Report errors
debug ‐ Debug output
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

ICA
-C --components=0
ICA components 0 = automatic estimationICA components 0 = automatic
estimation

--normalize
normalized ICs

--no-meanstrip
don't strip the mean from the mixing curves

-g --guess
use initial guess for myocardial perfusion

-s --segscale=1.4
segment and scale the crop box around the LV (0=no segmentation)segment and
scale the crop box around the LV (0=no segmentation)

-k --skip=0
skip images at the beginning of the series as they are of other
modalitiesskip images at the beginning of the series as they are of other
modalities

-m --max-ica-iter=400
maximum number of iterations in ICAmaximum number of iterations in ICA

-E --segmethod=features
Segmentation method
delta-peak ‐ difference of the peak enhancement images
features ‐ feature images
delta-feature ‐ difference of the feature images

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).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).

Registration
-c --cost=ssd
registration criterion

-O --optimizer=gsl:opt=simplex,step=1.0
Optimizer used for minimizationOptimizer used for minimization For
supported plugins see PLUGINS:minimizer/singlecost

-f --transForm=rigid
transformation typetransformation type For supported plugins see
PLUGINS:2dimage/transform

-l --mg-levels=3
multi-resolution levelsmulti-resolution levels

-R --reference=-1
Global reference all image should be aligned to. If set to a non-negative
value, the images will be aligned to this references, and the cropped output
image date will be injected into the original images. Leave at -1 if you
don't care. In this case all images with be registered to a mean position of
the movementGlobal reference all image should be aligned to. If set to a
non-negative value, the images will be aligned to this references, and the
cropped output image date will be injected into the original images. Leave
at -1 if you don't care. In this case all images with be registered to a
mean position of the movement

-P --passes=2
registration passesregistration passes

PLUGINS: 1d/splinebc


mirror Spline interpolation boundary conditions that mirror on the boundary

(no parameters)

repeat Spline interpolation boundary conditions that repeats the value at the boundary

(no parameters)

zero Spline interpolation boundary conditions that assumes zero for values outside

(no parameters)

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: 2dimage/transform


affine Affine transformation (six degrees of freedom)., supported parameters are:

imgboundary = mirror; factory
image interpolation boundary conditions. For supported plug-ins see
PLUGINS:1d/splinebc

imgkernel = [bspline:d=3]; factory
image interpolator kernel. For supported plug-ins see
PLUGINS:1d/splinekernel

rigid Rigid transformations (i.e. rotation and translation, three degrees of
freedom)., supported parameters are:

imgboundary = mirror; factory
image interpolation boundary conditions. For supported plug-ins see
PLUGINS:1d/splinebc

imgkernel = [bspline:d=3]; factory
image interpolator kernel. For supported plug-ins see
PLUGINS:1d/splinekernel

rot-center = [[0,0]]; 2dfvector
Relative rotation center, i.e. <0.5,0.5> corresponds to the center of the
support rectangle.

rotation Rotation transformations (i.e. rotation about a given center, one degree of
freedom)., supported parameters are:

imgboundary = mirror; factory
image interpolation boundary conditions. For supported plug-ins see
PLUGINS:1d/splinebc

imgkernel = [bspline:d=3]; factory
image interpolator kernel. For supported plug-ins see
PLUGINS:1d/splinekernel

rot-center = [[0,0]]; 2dfvector
Relative rotation center, i.e. <0.5,0.5> corresponds to the center of the
support rectangle.

spline Free-form transformation that can be described by a set of B-spline coefficients
and an underlying B-spline kernel., supported parameters are:

anisorate = [[0,0]]; 2dfvector
anisotropic coefficient rate in pixels, nonpositive values will be
overwritten by the 'rate' value..

imgboundary = mirror; factory
image interpolation boundary conditions. For supported plug-ins see
PLUGINS:1d/splinebc

imgkernel = [bspline:d=3]; factory
image interpolator kernel. For supported plug-ins see
PLUGINS:1d/splinekernel

kernel = [bspline:d=3]; factory
transformation spline kernel.. For supported plug-ins see
PLUGINS:1d/splinekernel

penalty = ; factory
Transformation penalty term. For supported plug-ins see
PLUGINS:2dtransform/splinepenalty

rate = 10; float in [1, inf)
isotropic coefficient rate in pixels.

translate Translation only (two degrees of freedom), supported parameters are:

imgboundary = mirror; factory
image interpolation boundary conditions. For supported plug-ins see
PLUGINS:1d/splinebc

imgkernel = [bspline:d=3]; factory
image interpolator kernel. For supported plug-ins see
PLUGINS:1d/splinekernel

vf This plug-in implements a transformation that defines a translation for each
point of the grid defining the domain of the transformation., supported
parameters are:

imgboundary = mirror; factory
image interpolation boundary conditions. For supported plug-ins see
PLUGINS:1d/splinebc

imgkernel = [bspline:d=3]; factory
image interpolator kernel. For supported plug-ins see
PLUGINS:1d/splinekernel

PLUGINS: 2dtransform/splinepenalty


divcurl divcurl penalty on the transformation, supported parameters are:

curl = 1; float in [0, inf)
penalty weight on curl.

div = 1; float in [0, inf)
penalty weight on divergence.

norm = 0; bool
Set to 1 if the penalty should be normalized with respect to the image
size.

weight = 1; float in (0, inf)
weight of penalty energy.

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 ofthe 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:
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 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-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).

maxiter = 100; int in [1, inf)
Stopping criterion: the maximum number of iterations.

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 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-2dmyomilles -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'.

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