DOKK / manpages / debian 10 / mia-tools / mia-2dmyoicapgt.1.en
mia-2dmyoicapgt(1) General Commands Manual mia-2dmyoicapgt(1)

('mia\-2dmyoicapgt',) - Run a registration of a series of 2D images.

mia-2dmyoicapgt -i <in-file> -o <out-file> [options]

mia-2dmyoicapgt This program implements a two passs motion compensation algorithm. First a linear registration is run based on a variation of Gupta et~al. "Fully automatic registration and segmentation of first-pass myocardial perfusion MR image sequences", Academic Radiology 17, 1375-1385 as described in 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, followed by a non-linear registration based Chao Li and Ying Sun, 'Nonrigid Registration of Myocardial Perfusion MRI Using Pseudo Ground Truth' , In Proc. Medical Image Computing and Computer-Assisted Intervention MICCAI 2009, 165-172, 2009. Note that for this nonlinear motion correction a preceding linear registration step is usually required. This version of the program may run all registrations in parallel.

spacial neighborhood penalty weight

temporal second derivative penalty weight

correlation threshold for neighborhood analysis

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

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
print copyright information

print this help

-? --usage
print a short help

print the version number and exit

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-peak ‐ difference of the peak enhancement images
features ‐ feature images
delta-feature ‐ difference of the 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. A value 0.0 forces the series to be identified as acquired with initial breath hold.

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).

Optimizer used for minimization of the linear registration The string value will be used to construct a plug-in.
For supported plugins see PLUGINS:minimizer/singlecost
linear transform to be used The string value will be used to construct a plug-in.
For supported plugins see PLUGINS:2dimage/transform
Optimizer used for minimization in the non-linear registration. 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.

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

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

linear registration passes (0 to disable)

non-linear registration passes (0 to disable)

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)

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.

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.

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.

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

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)

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

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.

This is the MIA implementation of the FastICA algorithm.

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

(no parameters)

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:
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.

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.

Register the perfusion series given in 'segment.set' by first using automatic ICA estimation to run the linear registration and then the PGT registration. Skip two images at the beginning and otherwiese use the default parameters. Store the result in 'registered.set'.

mia-2dmyoicapgt -i segment.set -o registered.set -k 2

Gert Wollny

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'.

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