mia-2dmyoica-nonrigid2(1)

2dmyoica-nonrigid2

Section 1 mia-tools bookworm source

Description

mia-2dmyoica-nonrigid2

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:

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’.