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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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