mia-2dmyomilles(1)
NAME
mia-2dmyomilles - Run a registration of a series of 2D images.
SYNOPSIS
mia-2dmyomilles -i <in-file> -o <out-file> [options]
DESCRIPTION
mia-2dmyomilles This program is use to run a modified version of the ICA based registration approach described in Milles et al. 'Fully Automated Motion Correction in First-Pass Myocardial Perfusion MR Image Sequences', Trans. Med. Imaging., 27(11), 1611-1621, 2008. Changes include the extraction of the quasi-periodic movement in free breathingly acquired data sets and the option to run affine or rigid registration instead of the optimization of translations only.
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=
file name base for registered files
--save-references=
save synthetic reference images to this file base
--save-cropped=
save cropped image set to this file
--save-feature=
save the features images resulting from the ICA and some
intermediate images used for the RV-LV segmentation with
the given file name base to PNG files. Also save the
coefficients of the initial best and the final IC mixing
matrix.
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
trace Function call trace
fail Report test failures
warning Warnings
error Report errors
debug Debug output
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
ICA
-C --components=0
ICA components 0 = automatic estimationICA components 0 =
automatic estimation
--normalize
normalized ICs
--no-meanstrip
don't strip the mean from the mixing curves
-g --guess
use initial guess for myocardial perfusion
-s --segscale=1.4
segment and scale the crop box around the LV (0=no
segmentation)segment and scale the crop box around the LV
(0=no segmentation)
-k --skip=0
skip images at the beginning of the series as they are of
other modalitiesskip images at the beginning of the
series as they are of other modalities
-m --max-ica-iter=400
maximum number of iterations in ICAmaximum number of
iterations in ICA
-E --segmethod=features
Segmentation method
delta-peak difference of the peak enhancement images
features feature images
delta-feature difference of the feature images
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).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
-c --cost=ssd
registration criterion
-O --optimizer=gsl:opt=simplex,step=1.0
Optimizer used for minimizationOptimizer used for
minimization For supported plugins see
PLUGINS:minimizer/singlecost
-f --transForm=rigid
transformation typetransformation type For supported
plugins see PLUGINS:2dimage/transform
-l --mg-levels=3
multi-resolution levelsmulti-resolution levels
-R --reference=-1
Global reference all image should be aligned to. If set
to a non-negative value, the images will be aligned to
this references, and the cropped output image date will
be injected into the original images. Leave at -1 if you
don't care. In this case all images with be registered to
a mean position of the movementGlobal reference all image
should be aligned to. If set to a non-negative value, the
images will be aligned to this references, and the
cropped output image date will be injected into the
original images. Leave at -1 if you don't care. In this
case all images with be registered to a mean position of
the movement
-P --passes=2
registration passesregistration passes
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/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:
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 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-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).
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
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 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-2dmyomilles -i segment.set -o registered.set -k 2
AUTHOR(s)
Gert Wollny
COPYRIGHT
This software is Copyright (c) 19992015 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'.
Opportunity
Personal Opportunity - Free software gives you access to billions of dollars of software at no cost. Use this software for your business, personal use or to develop a profitable skill. Access to source code provides access to a level of capabilities/information that companies protect though copyrights. Open source is a core component of the Internet and it is available to you. Leverage the billions of dollars in resources and capabilities to build a career, establish a business or change the world. The potential is endless for those who understand the opportunity.
Business Opportunity - Goldman Sachs, IBM and countless large corporations are leveraging open source to reduce costs, develop products and increase their bottom lines. Learn what these companies know about open source and how open source can give you the advantage.
Free Software
Free Software provides computer programs and capabilities at no cost but more importantly, it provides the freedom to run, edit, contribute to, and share the software. The importance of free software is a matter of access, not price. Software at no cost is a benefit but ownership rights to the software and source code is far more significant.
Free Office Software - The Libre Office suite provides top desktop productivity tools for free. This includes, a word processor, spreadsheet, presentation engine, drawing and flowcharting, database and math applications. Libre Office is available for Linux or Windows.
Free Books
The Free Books Library is a collection of thousands of the most popular public domain books in an online readable format. The collection includes great classical literature and more recent works where the U.S. copyright has expired. These books are yours to read and use without restrictions.
Source Code - Want to change a program or know how it works? Open Source provides the source code for its programs so that anyone can use, modify or learn how to write those programs themselves. Visit the GNU source code repositories to download the source.
Education
Study at Harvard, Stanford or MIT - Open edX provides free online courses from Harvard, MIT, Columbia, UC Berkeley and other top Universities. Hundreds of courses for almost all major subjects and course levels. Open edx also offers some paid courses and selected certifications.
Linux Manual Pages - A man or manual page is a form of software documentation found on Linux/Unix operating systems. Topics covered include computer programs (including library and system calls), formal standards and conventions, and even abstract concepts.
