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'.