Provided by: mia-tools_2.4.6-5build3_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); string
input perfusion data set
-o --out-file=(output, required); string
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:
trace ‐ Function call trace
debug ‐ Debug output
info ‐ Low level messages
message ‐ Normal messages
warning ‐ Warnings
fail ‐ Report test failures
error ‐ Report errors
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 in [0, 5]
Spline degree.
omoms OMoms-spline kernel creation, supported parameters are:
d = 3; int in [3, 3]
Spline degree.
PLUGINS: 2dimage/cost
lncc 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.
lsd Least-Squares Distance measure
(no parameters)
mi 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
ncc normalized cross correlation.
(no parameters)
ngf 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
ssd 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.
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.
sthresh = 0; double
Threshold intensity value for source image.
PLUGINS: 2dimage/fullcost
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.
labelimage
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.
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.
PLUGINS: 2dimage/io
bmp 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
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
PLUGINS: 2dimage/maskedcost
lncc 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.
mi 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
ncc normalized cross correlation with masking support.
(no parameters)
ssd Sum of squared differences with masking.
(no parameters)
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]]; 2dfvector
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]]; 2dfvector
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 in [1, inf)
isotropic coefficient rate in pixels.
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 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.
PLUGINS: minimizer/singlecost
gdas 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..
gdsq 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..
gsl 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:
simplex ‐ Simplex algorithm of Nelder and Mead
cg-fr ‐ Flecher-Reeves conjugate gradient algorithm
cg-pr ‐ Polak-Ribiere conjugate gradient algorithm
bfgs ‐ Broyden-Fletcher-Goldfarb-Shann
bfgs2 ‐ Broyden-Fletcher-Goldfarb-Shann (most efficient version)
gd ‐ Gradient descent.
step = 0.001; double in (0, inf)
initial step size.
tol = 0.1; double in (0, inf)
some tolerance parameter.
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 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-direct ‐ Dividing Rectangles
gn-direct-l ‐ Dividing Rectangles (locally biased)
gn-direct-l-rand ‐ Dividing Rectangles (locally biased, randomized)
gn-direct-noscal ‐ Dividing Rectangles (unscaled)
gn-direct-l-noscal ‐ Dividing Rectangles (unscaled, locally biased)
gn-direct-l-rand-noscale ‐ Dividing Rectangles (unscaled, locally biased, randomized)
gn-orig-direct ‐ Dividing Rectangles (original implementation)
gn-orig-direct-l ‐ Dividing Rectangles (original implementation, locally biased)
ld-lbfgs-nocedal ‐ None
ld-lbfgs ‐ Low-storage BFGS
ln-praxis ‐ Gradient-free Local Optimization via the Principal-Axis Method
ld-var1 ‐ Shifted Limited-Memory Variable-Metric, Rank 1
ld-var2 ‐ Shifted Limited-Memory Variable-Metric, Rank 2
ld-tnewton ‐ Truncated Newton
ld-tnewton-restart ‐ Truncated Newton with steepest-descent restarting
ld-tnewton-precond ‐ Preconditioned Truncated Newton
ld-tnewton-precond-restart ‐ Preconditioned Truncated Newton with steepest-descent
restarting
gn-crs2-lm ‐ Controlled Random Search with Local Mutation
ld-mma ‐ Method of Moving Asymptotes
ln-cobyla ‐ Constrained Optimization BY Linear Approximation
ln-newuoa ‐ Derivative-free Unconstrained Optimization by Iteratively Constructed
Quadratic Approximation
ln-newuoa-bound ‐ Derivative-free Bound-constrained Optimization by Iteratively
Constructed Quadratic Approximation
ln-neldermead ‐ Nelder-Mead simplex algorithm
ln-sbplx ‐ Subplex variant of Nelder-Mead
ln-bobyqa ‐ Derivative-free Bound-constrained Optimization
gn-isres ‐ Improved Stochastic Ranking Evolution Strategy
none ‐ don't specify algorithm
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-direct ‐ Dividing Rectangles
gn-direct-l ‐ Dividing Rectangles (locally biased)
gn-direct-l-rand ‐ Dividing Rectangles (locally biased, randomized)
gn-direct-noscal ‐ Dividing Rectangles (unscaled)
gn-direct-l-noscal ‐ Dividing Rectangles (unscaled, locally biased)
gn-direct-l-rand-noscale ‐ Dividing Rectangles (unscaled, locally biased, randomized)
gn-orig-direct ‐ Dividing Rectangles (original implementation)
gn-orig-direct-l ‐ Dividing Rectangles (original implementation, locally biased)
ld-lbfgs-nocedal ‐ None
ld-lbfgs ‐ Low-storage BFGS
ln-praxis ‐ Gradient-free Local Optimization via the Principal-Axis Method
ld-var1 ‐ Shifted Limited-Memory Variable-Metric, Rank 1
ld-var2 ‐ Shifted Limited-Memory Variable-Metric, Rank 2
ld-tnewton ‐ Truncated Newton
ld-tnewton-restart ‐ Truncated Newton with steepest-descent restarting
ld-tnewton-precond ‐ Preconditioned Truncated Newton
ld-tnewton-precond-restart ‐ Preconditioned Truncated Newton with steepest-descent
restarting
gn-crs2-lm ‐ Controlled Random Search with Local Mutation
ld-mma ‐ Method of Moving Asymptotes
ln-cobyla ‐ Constrained Optimization BY Linear Approximation
ln-newuoa ‐ Derivative-free Unconstrained Optimization by Iteratively Constructed
Quadratic Approximation
ln-newuoa-bound ‐ Derivative-free Bound-constrained Optimization by Iteratively
Constructed Quadratic Approximation
ln-neldermead ‐ Nelder-Mead simplex algorithm
ln-sbplx ‐ Subplex variant of Nelder-Mead
ln-bobyqa ‐ Derivative-free Bound-constrained Optimization
gn-isres ‐ Improved Stochastic Ranking Evolution Strategy
auglag ‐ Augmented Lagrangian algorithm
auglag-eq ‐ Augmented Lagrangian algorithm with equality constraints only
g-mlsl ‐ Multi-Level Single-Linkage (require local optimization and bounds)
g-mlsl-lds ‐ Multi-Level Single-Linkage (low-discrepancy-sequence, require local
gradient based optimization and bounds)
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.
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) 1999‐2015 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'.