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**PROGRAM:**

**NAME**

mia-2dmyoica-full - Run a registration of a series of 2D images.

**SYNOPSIS**

**mia-2dmyoica-full**

**-i**

**<in-file>**

**-o**

**<out-file>**

**[options]**

**DESCRIPTION**

**mia-2dmyoica-full**This program implements the 2D version of the motion compensation

algorithm described in Wollny G, Kellman P, Santos A, Ledesma-Carbayo M-J, "Automatic

Motion Compensation of Free Breathing acquired Myocardial Perfusion Data by using

Independent Component Analysis", Medical Image Analysis, 2012,

DOI:10.1016/j.media.2012.02.004. The software may first run a linear registration and then

a non-linear registration or just one of the both.This version of the program may run all

registrations in parallel.

**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 the registered images. Image type and numbering scheme

are taken from the input images as given in the input data set.

--save-cropped=(output); string

save cropped set to this file, the image files will use the stem of the name

as file name base

--save-feature=(output); string

save segmentation feature images and initial ICA mixing matrix

--save-refs=(output); string

for each registration pass save the reference images to files with the given

name base

--save-regs=(output); string

for each registration pass save intermediate registered images

**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

-s --segscale=0

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 e.g. because as they are of other

modalitiesskip 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 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

-b --min-breathing-frequency=-1

minimal mean frequency a mixing curve can have to be considered to stem from

brething. A healthy rest breating rate is 12 per minute. A negative value

disables the test. A value 0.0 forces the series to be indentified as

acquired with initial breath hold.minimal mean frequency a mixing curve can

have to be considered to stem from brething. A healthy rest breating rate is

12 per minute. A negative value disables the test. A value 0.0 forces the

series to be indentified as acquired with initial breath hold.

**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**

-L --linear-optimizer=gsl:opt=simplex,step=1.0

Optimizer used for minimization of the linear registration The string value

will be used to construct a plug-in. For supported plugins see

PLUGINS:minimizer/singlecost

--linear-transform=affine

linear transform to be used The string value will be used to construct a

plug-in. For supported plugins see PLUGINS:2dimage/transform

-O --non-linear-optimizer=gsl:opt=gd,step=0.1

Optimizer used for minimization in the non-linear registration. The string

value will be used to construct a plug-in. For supported plugins see

PLUGINS:minimizer/singlecost

-a --start-c-rate=16

start coefficinet rate in spines, gets divided by --c-rate-divider with

every pass.start coefficinet rate in spines, gets divided by

--c-rate-divider with every pass.

--c-rate-divider=2

Cofficient rate divider for each pass.Cofficient rate divider for each pass.

-d --start-divcurl=10000

Start divcurl weight, gets divided by --divcurl-divider with every

pass.Start divcurl weight, gets divided by --divcurl-divider with every

pass.

--divcurl-divider=2

Divcurl weight scaling with each new pass.Divcurl weight scaling with each

new pass.

-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

-w --imagecost=image:weight=1,cost=ssd

image cost, do not specify the src and ref parameters, these will be set by

the program. The string value will be used to construct a plug-in. For

supported plugins see PLUGINS:2dimage/fullcost

-l --mg-levels=3

multi-resolution levelsmulti-resolution levels

-p --linear-passes=3

linear registration passes (0 to disable)linear registration passes (0 to

disable)

-P --nonlinear-passes=3

non-linear registration passes (0 to disable)non-linear registration passes

(0 to disable)

**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/cost**

**lncc**local normalized cross correlation with masking support., supported parameters

are:

__w__= 5; uint in [1, 256]

half width of the window used for evaluating the localized cross

correlation.

**lsd**Least-Squares Distance measure

(no parameters)

**mi**Spline parzen based mutual information., supported parameters are:

__cut__= 0; float in [0, 40]

Percentage of pixels to cut at high and low intensities to remove

outliers.

__mbins__= 64; uint in [1, 256]

Number of histogram bins used for the moving image.

__mkernel__= [bspline:d=3]; factory

Spline kernel for moving image parzen hinstogram. For supported plug-ins

see PLUGINS:1d/splinekernel

__rbins__= 64; uint in [1, 256]

Number of histogram bins used for the reference image.

__rkernel__= [bspline:d=0]; factory

Spline kernel for reference image parzen hinstogram. For supported plug-

ins see PLUGINS:1d/splinekernel

**ncc**normalized cross correlation.

(no parameters)

**ngf**This function evaluates the image similarity based on normalized gradient

fields. Various evaluation kernels are availabe., supported parameters are:

__eval__= ds; dict

plugin subtype. Supported values are:

__sq__‐ square of difference

__ds__‐ square of scaled difference

__dot__‐ scalar product kernel

__cross__‐ cross product kernel

**ssd**2D imaga cost: sum of squared differences, supported parameters are:

__autothresh__= 0; float in [0, 1000]

Use automatic masking of the moving image by only takeing intensity values

into accound that are larger than the given threshold.

__norm__= 0; bool

Set whether the metric should be normalized by the number of image pixels.

**ssd-automask**

2D image cost: sum of squared differences, with automasking based on given

thresholds, supported parameters are:

__rthresh__= 0; double

Threshold intensity value for reference image.

__sthresh__= 0; double

Threshold intensity value for source image.

**PLUGINS:** **2dimage/fullcost**

**image**Generalized image similarity cost function that also handles multi-resolution

processing. The actual similarity measure is given es extra parameter.,

supported parameters are:

__cost__= ssd; factory

Cost function kernel. For supported plug-ins see PLUGINS:2dimage/cost

__debug__= 0; bool

Save intermediate resuts for debugging.

__ref__=(input, string)

Reference image.

__src__=(input, string)

Study image.

__weight__= 1; float

weight of cost function.

**labelimage**

Similarity cost function that maps labels of two images and handles label-

preserving multi-resolution processing., supported parameters are:

__debug__= 0; int in [0, 1]

write the distance transforms to a 3D image.

__maxlabel__= 256; int in [2, 32000]

maximum number of labels to consider.

__ref__=(input, string)

Reference image.

__src__=(input, string)

Study image.

__weight__= 1; float

weight of cost function.

**maskedimage**

Generalized masked image similarity cost function that also handles multi-

resolution processing. The provided masks should be densly filled regions in

multi-resolution procesing because otherwise the mask information may get lost

when downscaling the image. The reference mask and the transformed mask of the

study image are combined by binary AND. The actual similarity measure is given

es extra parameter., supported parameters are:

__cost__= ssd; factory

Cost function kernel. For supported plug-ins see

PLUGINS:2dimage/maskedcost

__ref__=(input, string)

Reference image.

__ref-mask__=(input, string)

Reference image mask (binary).

__src__=(input, string)

Study image.

__src-mask__=(input, string)

Study image mask (binary).

__weight__= 1; float

weight of cost function.

**PLUGINS:** **2dimage/io**

**bmp**BMP 2D-image input/output support

Recognized file extensions: .BMP, .bmp

Supported element types:

binary data, unsigned 8 bit, unsigned 16 bit

**datapool**Virtual IO to and from the internal data pool

Recognized file extensions: .@

**dicom**2D image io for DICOM

Recognized file extensions: .DCM, .dcm

Supported element types:

signed 16 bit, unsigned 16 bit

**exr**a 2dimage io plugin for OpenEXR images

Recognized file extensions: .EXR, .exr

Supported element types:

unsigned 32 bit, floating point 32 bit

**jpg**a 2dimage io plugin for jpeg gray scale images

Recognized file extensions: .JPEG, .JPG, .jpeg, .jpg

Supported element types:

unsigned 8 bit

**png**a 2dimage io plugin for png images

Recognized file extensions: .PNG, .png

Supported element types:

binary data, unsigned 8 bit, unsigned 16 bit

**raw**RAW 2D-image output support

Recognized file extensions: .RAW, .raw

Supported element types:

binary data, signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit,

signed 32 bit, unsigned 32 bit, floating point 32 bit, floating point 64

bit

**tif**TIFF 2D-image input/output support

Recognized file extensions: .TIF, .TIFF, .tif, .tiff

Supported element types:

binary data, unsigned 8 bit, unsigned 16 bit, unsigned 32 bit

**vista**a 2dimage io plugin for vista images

Recognized file extensions: .V, .VISTA, .v, .vista

Supported element types:

binary data, signed 8 bit, unsigned 8 bit, signed 16 bit, unsigned 16 bit,

signed 32 bit, unsigned 32 bit, floating point 32 bit, floating point 64

bit

**PLUGINS:** **2dimage/maskedcost**

**lncc**local normalized cross correlation with masking support., supported parameters

are:

__w__= 5; uint in [1, 256]

half width of the window used for evaluating the localized cross

correlation.

**mi**Spline parzen based mutual information with masking., supported parameters are:

__cut__= 0; float in [0, 40]

Percentage of pixels to cut at high and low intensities to remove

outliers.

__mbins__= 64; uint in [1, 256]

Number of histogram bins used for the moving image.

__mkernel__= [bspline:d=3]; factory

Spline kernel for moving image parzen hinstogram. For supported plug-ins

see PLUGINS:1d/splinekernel

__rbins__= 64; uint in [1, 256]

Number of histogram bins used for the reference image.

__rkernel__= [bspline:d=0]; factory

Spline kernel for reference image parzen hinstogram. For supported plug-

ins see PLUGINS:1d/splinekernel

**ncc**normalized cross correlation with masking support.

(no parameters)

**ssd**Sum of squared differences with masking.

(no parameters)

**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-2dmyoica-full -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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