Provided by: mia-tools_2.0.13-1_amd64
NAME
mia-2dmyoserial-nonrigid - Run a serial registration of a series of 2D images.
SYNOPSIS
mia-2dmyoserial-nonrigid -i <in-file> -o <out-file> [options] <PLUGINS:2dimage/fullcost>
DESCRIPTION
mia-2dmyoserial-nonrigid This program runs the non-rigid motion compensation registration of an perfusion image series. The registration is run in a serial manner, this is, only images in temporal succession are registered, and the obtained transformations are applied accumulated to reach full registration. See e.g. Wollny, G., Ledesma-Carbayo, M.J., Kellman, P., Santos, A. "A New Similarity Measure for Non-Rigid Breathing Motion Compensation of Myocardial Perfusion MRI ". Proc 30th Annual International IEEE EMBS Conference, pp. 3389-3392. Vancouver, Aug. 2008, doi:10.1109/IEMBS.2008.4649933,
OPTIONS
File-IO -i --in-file=(required) input perfusion data set -o --out-file=(required) output perfusion data set -R --registered=reg file name base for registered fiels Registration -O --optimizer=gsl:opt=gd,step=0.1 Optimizer used for minimization For supported plugins see PLUGINS:minimizer/singlecost -l --mg-levels=3 multi-resolution levels -f --transForm=spline:rate=16,penalty=[divcurl:weight=0.01] transformation type For supported plugins see PLUGINS:2dimage/transform -r --ref=-1 reference frame (-1 == use image in the middle) -k --skip=0 skip registration of these images at the beginning of the series 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 warning ‐ Warnings error ‐ Report errors fail ‐ Report test failures 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 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).
PLUGINS: 1d/splinebc
mirror Spline interpolation boundary conditions that mirror on the boundary (no parameters) repeat Spline interpolation boundary conditions that repeats the value at the boundary (no parameters) zero Spline interpolation boundary conditions that assumes zero for values outside (no parameters)
PLUGINS: 1d/splinekernel
bspline B-spline kernel creation , supported parameters are: d = 3 (int) Spline degree. in [0, 5] omoms OMoms-spline kernel creation, supported parameters are: d = 3 (int) Spline degree. in [3, 3]
PLUGINS: 2dimage/cost
lsd Least-Squares Distance measure (no parameters) mi Spline parzen based mutual information., supported parameters are: cut = 0 (float) Percentage of pixels to cut at high and low intensities to remove outliers. in [0, 40] mbins = 64 (uint) Number of histogram bins used for the moving image. in [1, 256] mkernel = [bspline:d=3] (factory) Spline kernel for moving image parzen hinstogram. For supported plug-ins see PLUGINS:1d/splinekernel rbins = 64 (uint) Number of histogram bins used for the reference image. in [1, 256] rkernel = [bspline:d=0] (factory) Spline kernel for reference image parzen hinstogram. For supported plug- ins see PLUGINS:1d/splinekernel ngf This function evaluates the image similarity based on normalized gradient fields. Various evaluation kernels are availabe., supported parameters are: eval = ds (string) plugin subtype (sq, ds,dot,cross). ssd 2D imaga cost: sum of squared differences, supported parameters are: norm = 0 (bool) Set whether the metric should be normalized by the number of image pixels.
PLUGINS: 2dimage/fullcost
divcurl divcurl penalty cost function, supported parameters are: curl = 1 (float) penalty weight on curl. in [0, 3.40282e+38] div = 1 (float) penalty weight on divergence. in [0, 3.40282e+38] weight = 1 (float) weight of cost function. in [-1e+10, 1e+10] 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 = ref.@ (io) Reference image. For supported file types see PLUGINS:2dimage/io src = src.@ (io) Study image. For supported file types see PLUGINS:2dimage/io weight = 1 (float) weight of cost function. in [-1e+10, 1e+10]
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: 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/transform
affine 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 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]] (streamable) Relative rotation center, i.e. <0.5,0.5> corresponds to the center of the support rectangle. rotation 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]] (streamable) Relative rotation center, i.e. <0.5,0.5> corresponds to the center of the support rectangle. spline 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) isotropic coefficient rate in pixels. in [1, 3.40282e+38] translate 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 vf 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
PLUGINS: 2dtransform/splinepenalty
divcurl divcurl penalty on the transformation, supported parameters are: curl = 1 (float) penalty weight on curl. in [0, 3.40282e+38] div = 1 (float) penalty weight on divergence. in [0, 3.40282e+38] weight = 1 (float) weight of penalty energy. in [0, 3.40282e+38]
PLUGINS: minimizer/singlecost
gdsq Gradient descent with quadratic step estimation, supported parameters are: ftolr = 0 (double) Stop if the relative change of the criterion is below.. in [0, INF] gtola = 0 (double) Stop if the inf-norm of the gradient is below this value.. in [0, INF] maxiter = 100 (uint) Stopping criterion: the maximum number of iterations. in [1, 2147483647] scale = 2 (double) Fallback fixed step size scaling. in [1, INF] step = 0.1 (double) Initial step size. in [0, INF] xtola = 0 (double) Stop if the inf-norm of x-update is below this value.. in [0, INF] 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) gradient based optimizers: stop when |grad| < eps, simplex: stop when simplex size < eps.. in [1e-10, 10] iter = 100 (int) maximum number of iterations. in [1, 2147483647] 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) initial step size. in [0, 10] tol = 0.1 (double) some tolerance parameter. in [0.001, 10] 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) Stopping criterion: the absolute change of the objective value is below this value. in [0, INF] ftolr = 0 (double) Stopping criterion: the relative change of the objective value is below this value. in [0, INF] higher = inf (double) Higher boundary (equal for all parameters). in [INF, INF] 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). in [INF, INF] maxiter = 100 (int) Stopping criterion: the maximum number of iterations. in [1, 2147483647] 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) Initial step size for gradient free methods. in [0, INF] stop = -inf (double) Stopping criterion: function value falls below this value. in [INF, INF] xtola = 0 (double) Stopping criterion: the absolute change of all x-values is below this value. in [0, INF] xtolr = 0 (double) Stopping criterion: the relative change of all x-values is below this value. in [0, INF]
EXAMPLE
Register the perfusion series given in 'segment.set' to reference image 30. Skip two images at the beginning and using mutual information as cost function, and penalize the transformation by divcurl with weight 5. Store the result in 'registered.set'. mia-2dmyoserial-nonrigid -i segment.set -o registered.set -k 2 -r 30 image:cost=mi -f spline:rate=5,penalty=[divcurl:weight=5]
AUTHOR(s)
Gert Wollny
COPYRIGHT
This software is Copyright (c) 1999‐2013 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'.