mia-2dmyoica-full(1)

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

🌸 NAME 🌸

mia-2dmyoica-full — run a registration of a series of 2D images. bestie this is THE 2D myo ICA full pipeline command and she is busy 💅

✨ SYNOPSIS ✨

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

💖 DESCRIPTION 💖

Okay so mia-2dmyoica-full is the program that implements the 2D version of the motion compensation algorithm described in:

Wollny G, Kellman P, Santos A, Ledesma-Carbayo M-J, "Automatic Motion Compensation of Free Breathing acquired Myocardial Perfusion Data by using Independent Component Analysis", Medical Image Analysis, 2012.

ngl that citation is iconic, put it on a t-shirt 🎀

The software may first run a linear registration and then a non-linear registration, or just one of the two. And here's the part that makes this version special: this version of the program can run all registrations in parallel. multitasking queen behavior fr fr 🏃‍♀️💨

🎀 OPTIONS 🎀

📁 File-IO 📁

  • -i, --in-file=(input, required); string — the input perfusion data set 🫶 (yes it's required, she needs food)
  • -o, --out-file=(output, required); string — the output perfusion data set ✨ (also required, don't forget her)
  • -r, --registered= — file name base for the registered images. The image type and the numbering scheme get taken from the input images as given in the input data set, so she's just borrowing the fit 💅
  • --save-cropped=(output); string — save the cropped set to this file; the image files will use the stem of the name as their file name base
  • --save-feature=(output); string — save segmentation feature images and the initial ICA mixing matrix 🧠✨
  • --save-refs=(output); string — for each registration pass, save the reference images to files using the given name base
  • --save-regs=(output); string — for each registration pass, save the intermediate registered images

🆘 Help & Info 🆘

  • -V, --verbose=warning — verbosity of output; she prints messages of the given level and higher priorities. 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 — the FastICA implementation to be used. 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 (she said leave the mean ALONE)
  • -s, --segscale=0 — segment and scale the crop box around the LV (0 = no segmentation)
  • -k, --skip=0 — skip this many images at the beginning of the series, e.g. because as they are of other modalities 🙈
  • -m, --max-ica-iter=400 — maximum number of iterations in ICA
  • -E, --segmethod=features — segmentation method:
    • delta-feature ‐ difference of the feature images
    • delta-peak ‐ difference of the peak enhancement images
    • features ‐ feature images
  • -b, --min-breathing-frequency=-1 — minimal mean frequency a mixing curve can have to be considered to stem from breathing. A healthy resting breathing rate is 12 per minute. A negative value disables the test. A value of 0.0 forces the series to be identified as acquired with initial breath hold. 🫁

⚙️ Processing ⚙️

  • --threads=-1 — maximum 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 🌀

  • -L, --linear-optimizer=gsl:opt=simplex,step=1.0 — optimizer used for minimization of the linear registration. The string value will be used to construct a plug-in. for supported plugins see PLUGINS:minimizer/singlecost
  • --linear-transform=affine — linear transform to be used. The string value will be used to construct a plug-in. for supported plugins see PLUGINS:2dimage/transform
  • -O, --non-linear-optimizer=gsl:opt=gd,step=0.1 — optimizer used for minimization in the non-linear registration. The string value will be used to construct a plug-in. for supported plugins see PLUGINS:minimizer/singlecost
  • -a, --start-c-rate=16 — start coefficient rate in spines; it gets divided by --c-rate-divider with every pass.
  • --c-rate-divider=2 — coefficient rate divider for each pass.
  • -d, --start-divcurl=10000 — start divcurl weight; it gets divided by --divcurl-divider with every pass.
  • --divcurl-divider=2 — divcurl weight scaling with each new pass.
  • -R, --reference=-1 — 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 this case all images will be registered to a mean position of the movement 🫶
  • -w, --imagecost=image:weight=1,cost=ssd — image cost. Do not specify the src and ref parameters, these will be set by the program. The string value will be used to construct a plug-in. for supported plugins see PLUGINS:2dimage/fullcost
  • -l, --mg-levels=3 — multi-resolution levels
  • -p, --linear-passes=3 — linear registration passes (0 to disable)
  • -P, --nonlinear-passes=3 — non-linear registration passes (0 to disable)

🧵 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 🌈

bestie these are the cost function glow-ups that also know how to do multi-resolution (aka the pyramid thing where we shrink the images down and go level by level 💅). The actual similarity measure itself? That's handed in as an extra parameter, iykyk.

✨ image ✨

A generalized image similarity cost function that also handles multi-resolution processing. The real similarity measure gets passed in as an extra parameter. Here's everything it supports:

  • cost = ssd; factory — the cost function kernel. Wanna know which plug-ins that can be? Peep PLUGINS:2dimage/cost 🫶
  • debug = 0; bool — saves the intermediate results so you can go snooping for debugging 🕵️‍♀️
  • ref =(input, io) — the reference image. For which file types are allowed, see PLUGINS:2dimage/io
  • src =(input, io) — the study image. For which file types are allowed, see PLUGINS:2dimage/io
  • weight = 1; float — how much this cost function counts, weight-wise ⚖️

🎀 labelimage 🎀

A similarity cost function that maps the labels of two images and handles label-preserving multi-resolution processing. (Yes queen, the labels stay intact through the pyramid, no cap.) Here's what it supports:

  • debug = 0; int in [0, 1] — writes the distance transforms out to a 3D image ✍️
  • maxlabel = 256; int in [2, 32000] — the maximum number of labels to even consider
  • ref =(input, io) — the reference image. For which file types are allowed, see PLUGINS:2dimage/io
  • src =(input, io) — the study image. For which file types are allowed, see PLUGINS:2dimage/io
  • weight = 1; float — how much this cost function counts, weight-wise ⚖️

🦋 maskedimage 🦋

A generalized masked image similarity cost function that also handles multi-resolution processing. The actual similarity measure gets passed in as an extra parameter. Here's what it supports:

🚨 real talk: the masks you provide should be densely filled regions when you're doing multi-resolution processing, because otherwise the mask info can literally get lost when the image gets downscaled. Gone. Vaporized. 😭

Also: the reference mask and the transformed mask of the study image get combined with a binary AND. That's the vibe.

  • cost = ssd; factory — the cost function kernel. For supported plug-ins see PLUGINS:2dimage/maskedcost
  • ref =(input, io) — the reference image. For which file types are allowed, see PLUGINS:2dimage/io
  • ref-mask =(input, io) — the reference image mask (binary). For which file types are allowed, see PLUGINS:2dimage/io
  • src =(input, io) — the study image. For which file types are allowed, see PLUGINS:2dimage/io
  • src-mask =(input, io) — the study image mask (binary). For which file types are allowed, see PLUGINS:2dimage/io
  • weight = 1; float — how much this cost function counts, weight-wise ⚖️

🌸 PLUGINS: 2dimage/io 🌸

the file-format girls. every one of them an icon in her own right 💖

🌈 bmp

BMP 2D-image input/output support. This plug-in reads and writes binary images and 8-bit gray scale images. Read-only support is there for 4-bit gray scale images. The color table? Ignored, bestie. The pixel values are just taken as literal gray scale values. Simple. Clean. Periodt.

  • 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 💅

cost functions that also respect your masks. so thoughtful of them 🥹

✨ lncc ✨

local normalized cross correlation, with masking support. Here's what it takes:

  • w = 5; uint in [1, 256] — half width of the window used when evaluating the localized cross correlation 🪟

💖 mi 💖

Spline parzen based mutual information, with masking. Its parameters:

  • cut = 0; float in [0, 40] — percentage of pixels to cut off at the high and low intensities, so the outliers get yeeted ✂️
  • mbins = 64; uint in [1, 256] — number of histogram bins used for the moving image
  • mkernel = [bspline:d=3]; factory — spline kernel for the moving image parzen histogram. 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 the reference image parzen histogram. 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 🔥

the transformation roster. pick your fighter 🥊

⭐ affine ⭐

Affine transformation, six degrees of freedom. Slay. Here's what she takes:

  • 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 — that's rotation plus translation, three degrees of freedom. Parameters:

  • 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. Like, <0.5,0.5> corresponds to the center of the support rectangle 🎯

🎀 rotation 🎀

Rotation transformations — rotation about a given center, one degree of freedom. Parameters:

  • 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. Like, <0.5,0.5> corresponds to the center of the support rectangle 🎯

🦋 spline 🦋

A free-form transformation that you can describe with a set of B-spline coefficients plus an underlying B-spline kernel. The flexible one. The main character, honestly 👑

  • anisorate = [[0,0]]; 2dfvector — anisotropic coefficient rate in pixels. Nonpositive values get overwritten by the rate value, no discussion.
  • 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. The simple one, and that's on minimalism ✨

  • 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. Every single point gets its own little translation, obsessed.

  • 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 💅✨

okay bestie, so 2dtransform/splinepenalty is giving one plugin vibe: divcurl, and it's literally the divcurl penalty on the transformation. the supported parameters are right here, no cap:

  • 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 🦋🌸

okay so FastICA has two implementations here, bestie, and both are iconic in their own way:

  • 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 but make it ✨self-correcting✨

bestie this one is gradient descent with automatic step size correction, and the params it fw are:

  • ftolr = 0 (double in [0, inf)) — stop if the relative change of the criterion is below. 💅
  • max-step = 2 (double in (0, inf)) — the 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)) — the 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, but it estimates the step, iconic

this one is gradient descent with quadratic step estimation, and it takes:

  • 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)) — the fallback fixed step size scaling.
  • step = 0.1 (double in (0, inf)) — the initial step size.
  • xtola = 0 (double in [0, inf)) — stop if the inf-norm of the x-update is below this value. 🦋

gsl — the GNU Scientific Library optimizer plug-in

this one is an optimizer plugin built on the multimin optimizers from the GNU Scientific Library (GSL), over at https://www.gnu.org/software/gsl/ — slay. its params:

  • eps = 0.01 (double in (0, inf)) — for the gradient based optimizers: stop when |grad| < eps; for simplex: stop when the simplex size < eps.
  • iter = 100 (uint in [1, inf)) — the maximum number of iterations.
  • opt = gd (dict) — which specific optimizer to use. the 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)) — the initial step size.
  • tol = 0.1 (double in (0, inf)) — some tolerance parameter. (yes really, that's the whole description, i'm not making stuff up 🫶)

nlopt — minimizer algorithms via the NLOPT library

for a description of the optimizers please see http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms. now the params, and bestie there are a lot:

  • 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) — the higher boundary (equal for all parameters).
  • local-opt = none (dict) — a 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) — the 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)) — the 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-2dmyoica-full   -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.

and that's the whole page bestie, you made it to the end 🎀 this little number is part of the USER COMMANDS crew, it's at version v2.4.7, and its page name is literally mia-2dmyoica-full(1) — go forth and register some hearts, ily, periodt 💅✨🦋

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