mia-2dmyoica-nonrigid-parallel - Online in the Cloud

PROGRAM:

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,
DOI:10.1016/j.media.2012.02.004.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=reg
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.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
--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
-O --optimizer=gsl:opt=gd,step=0.1
Optimizer used for minimization. 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.

-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 --passes=3
registration passesregistration passes

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: 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-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'.

Use mia-2dmyoica-nonrigid-parallel online using onworks.net services