mia-2dmyomilles(1)

📦
version 2.4.7 · 4 versions
, in ubuntu resolute, ubuntu stonking

✨ mia-2dmyomilles(1) ✨ — General Commands Manual 🎀

okay bestie, welcome to the mia-2dmyomilles manual. yes, that's a whole word. iykyk. this is the general commands manual page for it, and it is long, so buckle up 🦋

💖 NAME

mia-2dmyomilles — run a registration of a series of 2D images 🌸

🦋 SYNOPSIS

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

like... that's the whole vibe. in-file, out-file, sprinkle options on top 💅

🌈 DESCRIPTION

mia-2dmyomilles — this program is used 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.

and the changes, bestie, are:

  • extraction of the quasi-periodic movement in free-breathingly acquired data sets 🫁✨
  • the option to run affine or rigid registration instead of only optimizing translations

so yeah, it ate and left no crumbs 🔥

💅 OPTIONS

🎀 File-IO

  • -i, --in-file=(input, required); string — the input perfusion data set. required, so no skipping 🙈
  • -o, --out-file=(output, required); string — the output perfusion data set. also required 💖
  • -r, --registered= — file name base for registered files
  • --save-references= — save synthetic reference images to this file base
  • --save-cropped= — save the cropped image set to this file
  • --save-feature= — save the features images resulting from the ICA, plus some intermediate images used for the RV-LV segmentation, as PNG files, using the given file name base. It also saves the coefficients of the initial best IC mixing matrix and the final IC mixing matrix. literally saving the receipts 📸

🍓 Help & Info

  • -V, --verbose=warning — verbosity of output; prints messages of the given level and higher priorities. The supported priorities, starting at the 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

✨ ICA

  • --fastica=internal — which FastICA implementation to use. For supported plugins see PLUGINS:fastica/implementation
  • -C, --components=0 — ICA 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)
  • -k, --skip=0 — skip images at the beginning of the series, since they're of other modalities 👋
  • -m, --max-ica-iter=400 — maximum number of iterations in ICA
  • -E, --segmethod=features — segmentation method. the options are:
    • delta-feature — difference of the feature images
    • delta-peak — difference of the peak enhancement images
    • features — feature images

🔥 Processing

  • --threads=-1 — maximum number of threads to use for processing. This number should be lower than or equal to the number of logical processor cores in the machine. -1 means automatic estimation. be nice to your CPU bestie 💀

👑 Registration

  • -c, --cost=ssd — registration criterion
  • -O, --optimizer=gsl:opt=simplex,step=1.0 — optimizer used for minimization. For supported plugins see PLUGINS:minimizer/singlecost
  • -f, --transForm=rigid — transformation type. For supported plugins see PLUGINS:2dimage/transform
  • -l, --mg-levels=3 — multi-resolution levels
  • -R, --reference=-1 — the global reference all images should be aligned to. If set to a non-negative value, the images will be aligned to this reference, and the cropped output image data will be injected into the original images. Leave it at -1 if you don't care — in that case all images will be registered to a mean position of the movement.
  • -P, --passes=2 — registration passes

🧚 PLUGINS: 1d/splinebc

mirror

Spline interpolation boundary conditions that mirror on the boundary 🪞

(no parameters)

repeat

Spline interpolation boundary conditions that repeat the value at the boundary 🔁

(no parameters)

zero

Spline interpolation boundary conditions that assume 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

A 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: fastica/implementation

internal

This is the MIA implementation of the FastICA algorithm 🧠✨

(no parameters)

itpp

This is the IT++ implementation of the FastICA algorithm.

(no parameters)

gdas — gradient descent with automatic step size correction 🏃‍♀️

The supported parameters are:

  • ftolr = 0; double in [0, inf) — stop if the relative change of the criterion is below. (yes the original sentence just ends there, period and all, we respect it 💖)
  • max-step = 2; double in (0, inf) — the maximal absolute step size, like the biggest leap it's allowed to take.
  • maxiter = 200; uint in [1, inf) — stopping criterion: the maximum number of iterations.
  • min-step = 0.1; double in (0, inf) — the minimal absolute step size, so no baby steps below this.
  • 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 🦋

The 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 — the GNU Scientific Library optimizer plug (GSL) 👑

An optimizer plugin based on the multimin optimizers of the GNU Scientific Library (GSL) https://www.gnu.org/software/gsl/. The supported parameters are:

  • eps = 0.01; double in (0, inf) — for gradient based optimizers: stop when |grad| < eps; for simplex: stop when simplex size < eps.
  • iter = 100; uint in [1, inf) — maximum number of iterations.
  • opt = gd; dict — the 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 🔥

Bestie, for a description of the optimizers please see http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms. The 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 — 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
    • 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 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

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.

fr fr, that's the whole thing. no warranty, but all the vibes. 💅

That's a wrap bestie 🎀 this page lived in the USER COMMANDS section of the manual, version v2.4.7, filed under mia-2dmyomilles(1) — go forth and register those series, you absolute icon 👑✨

📦 where this page comes from