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mia-2dmyoica-nonrigid-parallel(1) General Commands Manual mia-2dmyoica-nonrigid-parallel(1)

NAME

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

SYNOPSIS

mia-2dmyoica-nonrigid-parallel -i <in-file> -o <out-file> [options]

DESCRIPTION

mia-2dmyoica-nonrigid-parallel 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.

This version of the program runs all registrations in parallel.

OPTIONS

File-IO

input perfusion data set

output perfusion data set

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 set to this file, the image files will use the stem of the name as file name base

save segmentation feature images and initial ICA mixing matrix

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

for each registration pass save intermediate registered images

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

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

skip images at the beginning of the series e.g. because 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
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.

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

Optimizer used for minimization. The string value will be used to construct a plug-in.
For supported plugins see PLUGINS:minimizer/singlecost
start coefficinet rate in spines, gets divided by --c-rate-divider with every pass.

Cofficient rate divider for each pass.

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

Divcurl weight scaling with each new pass.

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
multi-resolution levels

registration passes

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/cost

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.

Least-Squares Distance measure

(no parameters)
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

normalized cross correlation.

(no parameters)
This function evaluates the image similarity based on normalized gradient fields. Various evaluation kernels are available., 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

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.

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

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, io)
Reference image. For supported file types see PLUGINS:2dimage/io

src =(input, io)
Study image. For supported file types see PLUGINS:2dimage/io

weight = 1; float
weight of cost function.

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, io)
Reference image. For supported file types see PLUGINS:2dimage/io

src =(input, io)
Study image. For supported file types see PLUGINS:2dimage/io

weight = 1; float
weight of cost function.

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, io)
Reference image. For supported file types see PLUGINS:2dimage/io

ref-mask =(input, io)
Reference image mask (binary). For supported file types see PLUGINS:2dimage/io

src =(input, io)
Study image. For supported file types see PLUGINS:2dimage/io

src-mask =(input, io)
Study image mask (binary). For supported file types see PLUGINS:2dimage/io

weight = 1; float
weight of cost function.

PLUGINS: 2dimage/io

BMP 2D-image input/output support. The plug-in supports reading and writing of binary images and 8-bit gray scale images. read-only support is provided for 4-bit gray scale images. The color table is ignored and the pixel values are taken as literal gray scale values.

Recognized file extensions: .BMP, .bmp

Supported element types:
binary data, unsigned 8 bit

Virtual IO to and from the internal data pool

Recognized file extensions: .@

2D image io for DICOM

Recognized file extensions: .DCM, .dcm

Supported element types:
signed 16 bit, unsigned 16 bit

a 2dimage io plugin for OpenEXR images

Recognized file extensions: .EXR, .exr

Supported element types:
unsigned 32 bit, floating point 32 bit

a 2dimage io plugin for jpeg gray scale images

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

Supported element types:
unsigned 8 bit

a 2dimage io plugin for png images

Recognized file extensions: .PNG, .png

Supported element types:
binary data, unsigned 8 bit, unsigned 16 bit

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

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

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

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.

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

normalized cross correlation with masking support.

(no parameters)
Sum of squared differences with masking.

(no parameters)

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-2dmyoica-nonrigid-parallel -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