Scroll to navigation

mia-2dmyomilles(1) General Commands Manual mia-2dmyomilles(1)

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

input perfusion data set

output perfusion data set

file name base for registered files

save synthetic reference images to this file base

save cropped image set to this file

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

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
print copyright information

print this help

-? --usage
print a short help

print the version number and exit

ICA

FastICA implementationto be used
For supported plugins see PLUGINS:fastica/implementation
ICA components 0 = automatic estimation

normalized ICs

don't strip the mean from the mixing curves

use initial guess for myocardial perfusion

segment and scale the crop box around the LV (0=no segmentation)

skip images at the beginning of the series as they are of other modalities

maximum number of iterations in ICA

Segmentation method

delta-feature ‐ difference of the feature images
delta-peak ‐ difference of the peak enhancement images
features ‐ feature images

Processing

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

registration criterion

Optimizer used for minimization
For supported plugins see PLUGINS:minimizer/singlecost
transformation type
For supported plugins see PLUGINS:2dimage/transform
multi-resolution levels

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 movement

registration passes

PLUGINS: 1d/splinebc

Spline interpolation boundary conditions that mirror on the boundary

(no parameters)
Spline interpolation boundary conditions that repeats the value at the boundary

(no parameters)
Spline interpolation boundary conditions that assumes zero for values outside

(no parameters)

PLUGINS: 1d/splinekernel

B-spline kernel creation , supported parameters are:

d = 3; int in [0, 5]
Spline degree.

OMoms-spline kernel creation, supported parameters are:

d = 3; int in [3, 3]
Spline degree.

PLUGINS: 2dimage/transform

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 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 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.

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.

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

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 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: fastica/implementation

This is the MIA implementation of the FastICA algorithm.

(no parameters)
This is the IT++ implementation of the FastICA algorithm.

(no parameters)

PLUGINS: minimizer/singlecost

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..

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..

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.

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:
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
auglag ‐ Augmented Lagrangian algorithm
auglag-eq ‐ Augmented Lagrangian algorithm with equality constraints only
g-mlsl ‐ Multi-Level Single-Linkage (require local optimization and bounds)
g-mlsl-lds ‐ Multi-Level Single-Linkage (low-discrepancy-sequence, require local gradient based optimization and bounds)
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'.

v2.4.7 USER COMMANDS