Provided by: mia-tools_2.4.6-4ubuntu2_amd64

**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-parallelThis 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 imagesHelp&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 messagestrace‐ Function call tracefail‐ Report test failureswarning‐ Warningserror‐ Report errorsdebug‐ Debug outputmessage‐ Normal messagesfatal‐ Report only fatal errors --copyright print copyright information -h --help print this help -? --usage print a short help --version print the version number and exitICA--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 methoddelta-peak‐ difference of the peak enhancement imagesfeatures‐ feature imagesdelta-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.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. 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. --c-rate-divider=2 Cofficient rate divider for each pass. -d --start-divcurl=10000 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, 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 levels -P --passes=3 registration passes

**PLUGINS:** **1d/splinekernel**

bsplineB-spline kernel creation , supported parameters are:d= 3; int in [0, 5] Spline degree.omomsOMoms-spline kernel creation, supported parameters are:d= 3; int in [3, 3] Spline degree.

**PLUGINS:** **2dimage/cost**

lncclocal 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.lsdLeast-Squares Distance measure (no parameters)miSpline 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/splinekernelrbins= 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/splinekernelnccnormalized cross correlation. (no parameters)ngfThis 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 differenceds‐ square of scaled differencedot‐ scalar product kernelcross‐ cross product kernelssd2D 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-automask2D 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**

imageGeneralized 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/costdebug= 0; bool Save intermediate resuts for debugging.ref=(input, io) Reference image. For supported file types see PLUGINS:2dimage/iosrc=(input, io) Study image. For supported file types see PLUGINS:2dimage/ioweight= 1; float weight of cost function.labelimageSimilarity 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/iosrc=(input, io) Study image. For supported file types see PLUGINS:2dimage/ioweight= 1; float weight of cost function.maskedimageGeneralized 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/maskedcostref=(input, io) Reference image. For supported file types see PLUGINS:2dimage/ioref-mask=(input, io) Reference image mask (binary). For supported file types see PLUGINS:2dimage/iosrc=(input, io) Study image. For supported file types see PLUGINS:2dimage/iosrc-mask=(input, io) Study image mask (binary). For supported file types see PLUGINS:2dimage/ioweight= 1; float weight of cost function.

**PLUGINS:** **2dimage/io**

bmpBMP 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 bitdatapoolVirtual IO to and from the internal data pool ('Recognized file extensions: ', '.@')dicom2D image io for DICOM ('Recognized file extensions: ', '.DCM, .dcm') Supported element types: signed 16 bit, unsigned 16 bitexra 2dimage io plugin for OpenEXR images ('Recognized file extensions: ', '.EXR, .exr') Supported element types: unsigned 32 bit, floating point 32 bitjpga 2dimage io plugin for jpeg gray scale images ('Recognized file extensions: ', '.JPEG, .JPG, .jpeg, .jpg') Supported element types: unsigned 8 bitpnga 2dimage io plugin for png images ('Recognized file extensions: ', '.PNG, .png') Supported element types: binary data, unsigned 8 bit, unsigned 16 bitrawRAW 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 bittifTIFF 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 bitvistaa 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**

lncclocal 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.miSpline 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/splinekernelrbins= 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/splinekernelnccnormalized cross correlation with masking support. (no parameters)ssdSum of squared differences with masking. (no parameters)

**PLUGINS:** **fastica/implementation**

internalThis is the MIA implementation of the FastICA algorithm. (no parameters)itppThis is the IT++ implementation of the FastICA algorithm. (no parameters)

**PLUGINS:** **minimizer/singlecost**

gdasGradient 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..gdsqGradient 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..gsloptimizer 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-Shannbfgs2‐ Broyden-Fletcher-Goldfarb-Shann (most efficient version)cg-fr‐ Flecher-Reeves conjugate gradient algorithmgd‐ Gradient descent.simplex‐ Simplex algorithm of Nelder and Meadcg-pr‐ Polak-Ribiere conjugate gradient algorithmstep= 0.001; double in (0, inf) initial step size.tol= 0.1; double in (0, inf) some tolerance parameter.nloptMinimizer 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 Strategyld-tnewton‐ Truncated Newtongn-direct-l-rand‐ Dividing Rectangles (locally biased, randomized)ln-newuoa‐ Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximationgn-direct-l-rand-noscale‐ Dividing Rectangles (unscaled, locally biased, randomized)gn-orig-direct‐ Dividing Rectangles (original implementation)ld-tnewton-precond‐ Preconditioned Truncated Newtonld-tnewton-restart‐ Truncated Newton with steepest-descent restartinggn-direct‐ Dividing Rectanglesln-neldermead‐ Nelder-Mead simplex algorithmln-cobyla‐ Constrained Optimization BY Linear Approximationgn-crs2-lm‐ Controlled Random Search with Local Mutationld-var2‐ Shifted Limited-Memory Variable-Metric, Rank 2ld-var1‐ Shifted Limited-Memory Variable-Metric, Rank 1ld-mma‐ Method of Moving Asymptotesld-lbfgs-nocedal‐ Noneld-lbfgs‐ Low-storage BFGSgn-direct-l‐ Dividing Rectangles (locally biased)none‐ don't specify algorithmln-bobyqa‐ Derivative-free Bound-constrained Optimizationln-sbplx‐ Subplex variant of Nelder-Meadln-newuoa-bound‐ Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximationln-praxis‐ Gradient-free Local Optimization via the Principal-Axis Methodgn-direct-noscal‐ Dividing Rectangles (unscaled)ld-tnewton-precond-restart‐ Preconditioned Truncated Newton with steepest-descent restartinglower= -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 Strategyld-tnewton‐ Truncated Newtongn-direct-l-rand‐ Dividing Rectangles (locally biased, randomized)ln-newuoa‐ Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximationgn-direct-l-rand-noscale‐ Dividing Rectangles (unscaled, locally biased, randomized)gn-orig-direct‐ Dividing Rectangles (original implementation)ld-tnewton-precond‐ Preconditioned Truncated Newtonld-tnewton-restart‐ Truncated Newton with steepest-descent restartinggn-direct‐ Dividing Rectanglesauglag-eq‐ Augmented Lagrangian algorithm with equality constraints onlyln-neldermead‐ Nelder-Mead simplex algorithmln-cobyla‐ Constrained Optimization BY Linear Approximationgn-crs2-lm‐ Controlled Random Search with Local Mutationld-var2‐ Shifted Limited-Memory Variable-Metric, Rank 2ld-var1‐ Shifted Limited-Memory Variable-Metric, Rank 1ld-mma‐ Method of Moving Asymptotesld-lbfgs-nocedal‐ Noneg-mlsl‐ Multi-Level Single-Linkage (require local optimization and bounds)ld-lbfgs‐ Low-storage BFGSgn-direct-l‐ Dividing Rectangles (locally biased)ln-bobyqa‐ Derivative-free Bound-constrained Optimizationln-sbplx‐ Subplex variant of Nelder-Meadln-newuoa-bound‐ Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximationauglag‐ Augmented Lagrangian algorithmln-praxis‐ Gradient-free Local Optimization via the Principal-Axis Methodgn-direct-noscal‐ Dividing Rectangles (unscaled)ld-tnewton-precond-restart‐ Preconditioned Truncated Newton with steepest-descent restartingld-slsqp‐ Sequential Least-Squares Quadratic Programmingstep= 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'.