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'.