---
name: "mia-2dmyoserial-nonrigid"
section: "1"
platform: "linux"
package: "mia-tools"
version: "2.4.7-11+b1"
release: "Debian 12 (Bookworm)"
platforms: ["linux"]
url: "https://new.carta.tech/mia-2dmyoserial-nonrigid"
---

# mia-2dmyoserial-nonrigid(1)

Run a serial registration of a series of 2D images.

## 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:

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. ⟨https://doi.org/10.1109/IEMBS.2008.4649933⟩

## 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=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'.