Provided by: mia-tools_2.4.7-13_amd64 
NAME
mia-2dmyoica-nonrigid - Run a registration of a series of 2D images.
SYNOPSIS
mia-2dmyoica-nonrigid -i <in-file> -o <out-file> [options]
DESCRIPTION
mia-2dmyoica-nonrigid This program implements 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.
⟨https://doi.org/10.1016/j.media.2012.02.004⟩
OPTIONS
File-IO
-i --in-file=(required, input); string
input perfusion data set
-o --out-file=(required, output); 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 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.
--save-refs=
save synthetic reference images
--save-regs=
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:
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
ICA
--fastica=internal
FastICA implementationto be used
For supported plugins see PLUGINS:fastica/implementation
-C --components=0
ICA 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)
-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
-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.
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).
Registration
-O --optimizer=gsl:opt=gd,step=0.1
Optimizer used for minimization
For supported plugins see PLUGINS:minimizer/singlecost
-R --refiner=
optimizer used for refinement after the main optimizer was called
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
--c-rate-divider=2
cofficient rate divider for each pass
-d --start-divcurl=10
start divcurl weight, gets divided by --divcurl-divider with every pass
--divcurl-divider=2
divcurl weight scaling with each new pass
-w --imagecost=image:weight=1,cost=ssd
image cost
For supported plugins see PLUGINS:2dimage/fullcost
-l --mg-levels=3
multi-resolution levels
-P --passes=5
registration 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 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
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, 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.
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, 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.
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, 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 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
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: 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:
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 -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'.