mia-2dmyoperiodic-nonrigid (1)

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

mia-2dmyoperiodic-nonrigid This program runs the non-rigid registration of an perfusion image series preferable acquired letting the patient breath freely. The registration algorithm implementes G. Wollny, M-J Ledesma-Cabryo, P.Kellman, and A.Santos, "Exploiting Quasiperiodicity in Motion Correction of Free-Breathing," IEEE Transactions on Medical Imaging, 29(8), 2010

File-IO

-i --in-file=(input,required)

input perfusion data set

-o --out-file=(output,required)

output perfusion data set

-r --registered=reg

file name base for registered fiels

--save-references

Save synthetic references to files refXXXX.v

Preconditions

-k --skip=0

Skip images at the begin of the series

--max-candidates=20

maximum number of candidates for global reference image

-S --cost-series=image:cost=[ngf:eval=ds]

Const function to use for the analysis of the series For supported plugins see PLUGINS:2dimage/fullcost

--ref-idx=

save reference index number to this file

-R --global-reference=-1

save reference index number to this file

-D --max-subset-delta=0

Maximum delta between two elements of the prealigned subset

Registration

-O --optimizer=gsl:opt=gd,step=0.01

Optimizer used for minimization For supported plugins see PLUGINS:minimizer/singlecost

--refiner=

optimizer used for additional minimization For supported plugins see PLUGINS:minimizer/singlecost

-l --mr-levels=3

multi-resolution levels

-f --transForm=spline:rate=16,penalty=[divcurl:weight=0.01]

transformation type For supported plugins see PLUGINS:2dimage/transform

-1 --cost-subset=image:cost=[ngf:eval=ds]

Cost function for registration during the subset registration For supported plugins see PLUGINS:2dimage/fullcost

-2 --cost-final=image:cost=ssd

Cost function for registration during the final registration For supported plugins see PLUGINS:2dimage/fullcost

Help & Info

-V --verbose=warning

verbosity of output, print messages of given level and higher priorities. Supported priorities starting at lowest level are:

info \(hy Low level messages

trace \(hy Function call trace

fail \(hy Report test failures

warning \(hy Warnings

error \(hy Report errors

debug \(hy Debug output

message \(hy Normal messages

fatal \(hy 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).

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)

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]

lncc

local normalized cross correlation with masking support., supported parameters are:

w = 5 (uint)

half width of the window used for evaluating the localized cross correlation. in [1, 256]

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

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 \(hy square of difference

ds \(hy square of scaled difference

dot \(hy scalar product kernel

cross \(hy cross product kernel

ssd

2D imaga cost: sum of squared differences, supported parameters are:

autothresh = 0 (float)

Use automatic masking of the moving image by only takeing intensity values into accound that are larger than the given threshold. in [0, 1000]

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. in [-1.79769e+308, 1.79769e+308]

sthresh = 0 (double)

Threshold intensity value for source image. in [-1.79769e+308, 1.79769e+308]

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. in [-1e+10, 1e+10]

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. in [-1e+10, 1e+10]

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

lncc

local normalized cross correlation with masking support., supported parameters are:

w = 5 (uint)

half width of the window used for evaluating the localized cross correlation. in [1, 256]

mi

Spline parzen based mutual information with masking., 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

ncc

normalized cross correlation with masking support.

(no parameters)

ssd

Sum of squared differences with masking.

(no parameters)

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

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]

norm = 0 (bool)

Set to 1 if the penalty should be normalized with respect to the image size.

weight = 1 (float)

weight of penalty energy. in [0, 3.40282e+38]

gdas

Gradient descent with automatic step size correction., supported parameters are:

ftolr = 0 (double)

Stop if the relative change of the criterion is below.. in [0, INF]

max-step = 2 (double)

Minimal absolute step size. in [1, INF]

maxiter = 200 (uint)

Stopping criterion: the maximum number of iterations. in [1, 2147483647]

min-step = 0.1 (double)

Maximal absolute step size. in [1e-10, INF]

xtola = 0.01 (double)

Stop if the inf-norm of the change applied to x is below this value.. in [0, INF]

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 \(hy Broyden-Fletcher-Goldfarb-Shann

bfgs2 \(hy Broyden-Fletcher-Goldfarb-Shann (most efficient version)

cg-fr \(hy Flecher-Reeves conjugate gradient algorithm

gd \(hy Gradient descent.

simplex \(hy Simplex algorithm of Nelder and Mead

cg-pr \(hy 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 \(hy Dividing Rectangles (original implementation, locally biased)

gn-direct-l-noscal \(hy Dividing Rectangles (unscaled, locally biased)

gn-isres \(hy Improved Stochastic Ranking Evolution Strategy

ld-tnewton \(hy Truncated Newton

gn-direct-l-rand \(hy Dividing Rectangles (locally biased, randomized)

ln-newuoa \(hy Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation

gn-direct-l-rand-noscale \(hy Dividing Rectangles (unscaled, locally biased, randomized)

gn-orig-direct \(hy Dividing Rectangles (original implementation)

ld-tnewton-precond \(hy Preconditioned Truncated Newton

ld-tnewton-restart \(hy Truncated Newton with steepest-descent restarting

gn-direct \(hy Dividing Rectangles

ln-neldermead \(hy Nelder-Mead simplex algorithm

ln-cobyla \(hy Constrained Optimization BY Linear Approximation

gn-crs2-lm \(hy Controlled Random Search with Local Mutation

ld-var2 \(hy Shifted Limited-Memory Variable-Metric, Rank 2

ld-var1 \(hy Shifted Limited-Memory Variable-Metric, Rank 1

ld-mma \(hy Method of Moving Asymptotes

ld-lbfgs-nocedal \(hy None

ld-lbfgs \(hy Low-storage BFGS

gn-direct-l \(hy Dividing Rectangles (locally biased)

none \(hy don't specify algorithm

ln-bobyqa \(hy Derivative-free Bound-constrained Optimization

ln-sbplx \(hy Subplex variant of Nelder-Mead

ln-newuoa-bound \(hy Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation

ln-praxis \(hy Gradient-free Local Optimization via the Principal-Axis Method

gn-direct-noscal \(hy Dividing Rectangles (unscaled)

ld-tnewton-precond-restart \(hy 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 \(hy Dividing Rectangles (original implementation, locally biased)

g-mlsl-lds \(hy Multi-Level Single-Linkage (low-discrepancy-sequence, require local gradient based optimization and bounds)

gn-direct-l-noscal \(hy Dividing Rectangles (unscaled, locally biased)

gn-isres \(hy Improved Stochastic Ranking Evolution Strategy

ld-tnewton \(hy Truncated Newton

gn-direct-l-rand \(hy Dividing Rectangles (locally biased, randomized)

ln-newuoa \(hy Derivative-free Unconstrained Optimization by Iteratively Constructed Quadratic Approximation

gn-direct-l-rand-noscale \(hy Dividing Rectangles (unscaled, locally biased, randomized)

gn-orig-direct \(hy Dividing Rectangles (original implementation)

ld-tnewton-precond \(hy Preconditioned Truncated Newton

ld-tnewton-restart \(hy Truncated Newton with steepest-descent restarting

gn-direct \(hy Dividing Rectangles

auglag-eq \(hy Augmented Lagrangian algorithm with equality constraints only

ln-neldermead \(hy Nelder-Mead simplex algorithm

ln-cobyla \(hy Constrained Optimization BY Linear Approximation

gn-crs2-lm \(hy Controlled Random Search with Local Mutation

ld-var2 \(hy Shifted Limited-Memory Variable-Metric, Rank 2

ld-var1 \(hy Shifted Limited-Memory Variable-Metric, Rank 1

ld-mma \(hy Method of Moving Asymptotes

ld-lbfgs-nocedal \(hy None

g-mlsl \(hy Multi-Level Single-Linkage (require local optimization and bounds)

ld-lbfgs \(hy Low-storage BFGS

gn-direct-l \(hy Dividing Rectangles (locally biased)

ln-bobyqa \(hy Derivative-free Bound-constrained Optimization

ln-sbplx \(hy Subplex variant of Nelder-Mead

ln-newuoa-bound \(hy Derivative-free Bound-constrained Optimization by Iteratively Constructed Quadratic Approximation

auglag \(hy Augmented Lagrangian algorithm

ln-praxis \(hy Gradient-free Local Optimization via the Principal-Axis Method

gn-direct-noscal \(hy Dividing Rectangles (unscaled)

ld-tnewton-precond-restart \(hy Preconditioned Truncated Newton with steepest-descent restarting

ld-slsqp \(hy 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]

Register the perfusion series given in 'segment.set'. Skip two images at the beginning, usa spline transformation of a knot rate 16 pixels, and penalize the transformation by divcurl with weight 5. Store the result in 'registered.set'.

mia-2dmyoperiodic-nonrigid -i segment.set -o registered.set -k 2 -d 5 -f spline:rate=16,penalty=[divcurl:weight=5]

Gert Wollny

This software is Copyright (c) 1999\(hy2013 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'.