mia-2dmyoicapgt(1)

📦
version 2.4.7 · 2 versions
, in ubuntu noble, ubuntu resolute, ubuntu stonking

✨ NAME ✨

okay so bestie, meet mia-2dmyoicapgt(1) — it runs a registration of a series of 2D images. No cap, it's literally the moment. 💖

🌸 SYNOPSIS 🌸

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

🦋 DESCRIPTION 🦋

So mia-2dmyoicapgt implements a two passs motion compensation algorithm. (Yes, "passs" with three s's, we don't question it.) First a linear registration is run based on a variation of

Gupta et al. "Fully automatic registration and segmentation of first-pass myocardial perfusion MR image sequences", Academic Radiology 17, 1375-1385

as 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,

followed by a non-linear registration based on

Chao Li and Ying Sun, "Nonrigid Registration of Myocardial Perfusion MRI Using Pseudo Ground Truth", In Proc. Medical Image Computing and Computer-Assisted Intervention MICCAI 2009, 165-172, 2009.

Note that for this nonlinear motion correction a preceding linear registration step is usually required. This version of the program may run all registrations in parallel. 💅

👑 OPTIONS 👑

✨ Pseudo Ground Thruth estimation ✨

  • -A, --alpha=0.1 — spacial neighborhood penalty weight 💖 (the little weight that keeps your neighbors in check, bestie)
  • -B, --beta=4 — temporal second derivative penalty weight ⏳ (it's giving "second derivative but make it penalty")
  • -T, --rho-thresh=0.85 — correlation threshold for neighborhood analysis 📊 (the correlation cutoff for when the neighborhood gets analyzed)

📁 File-IO 📁

  • -i, --in-file=(input, required); string — input perfusion data set 💾 (required, bestie, you gotta feed it something)
  • -o, --out-file=(output, required); string — output perfusion data set 💾 (required, where the magic lands)
  • -r, --registered= — File name base for the registered images. Image type and numbering scheme are taken from the input images as given in the input data set. 🖼️ (the base name for your registered pics, and it steals the type and numbering from the input data set)
  • --save-cropped=(output); string — save cropped set to this file, the image files will use the stem of the name as file name base ✂️ (crop it, save it, and the stem becomes the file name base)
  • --save-feature=(output); string — save segmentation feature images and initial ICA mixing matrix 🧠 (feature images plus the initial ICA mixing matrix, slay)
  • --save-refs=(output); string — for each registration pass save the reference images to files with the given name base 📌 (every registration pass gets its reference images saved)
  • --save-regs=(output); string — for each registration pass save intermediate registered images 🌀 (intermediate registered images for each pass)

🆘 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 🔢

🧬 ICA 🧬

  • --fastica=internal — FastICA implementationto be used 🧪 For supported plugins see PLUGINS:fastica/implementation
  • -C, --components=0 — ICA components 0 = automatic estimation 🔢 (zero means automatic, iykyk)
  • --normalize — normalized ICs ✨
  • --no-meanstrip — don't strip the mean from the mixing curves 🚫 (mean stays, periodt)
  • -s, --segscale=0 — segment and scale the crop box around the LV (0=no segmentation) 🫀 (zero means no segmentation, bestie)
  • -k, --skip=0 — skip images at the beginning of the series e.g. because as they are of other modalities ⏭️ (skip the first few, maybe they're 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 brething. A healthy rest breating rate is 12 per minute. A negative value disables the test. A value 0.0 forces the series to be identified as acquired with initial breath hold. 🫁 (breathing vibes only; negative disables, 0.0 forces initial breath hold)

⚙️ 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). 🧵 (max threads, and -1 means 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 coefficinet rate in spines, gets divided by --c-rate-divider with every pass. 📉
  • --c-rate-divider=2 — Cofficient rate divider for each pass. ➗
  • -d, --start-divcurl=10000 — Start divcurl weight, gets divided by --divcurl-divider with every pass. ⚖️
  • --divcurl-divider=2 — Divcurl weight scaling with each new pass. 📏
  • -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 movement 🎯 (set -1 if you don't care, and everything aligns to a mean position; non-negative means align to this reference and inject the cropped output image date into the originals)
  • -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 0️⃣
    • (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 does multi-resolution processing, bestie — the actual similarity measure gets handed in as an extra parameter. Here's what you can pass:

  • cost = ssd; factory — the cost function kernel. For supported plug-ins see PLUGINS:2dimage/cost 💖
  • debug = 0; bool — save the intermediate results so you can snoop on them for debugging 🕵️♀️
  • ref =(input, io) — the reference image. For supported file types see PLUGINS:2dimage/io
  • src =(input, io) — the study image. For supported file types see PLUGINS:2dimage/io
  • weight = 1; float — the weight of the cost function ⚖️

labelimage 🏷️

Similarity cost function that maps the labels of two images, and it handles label-preserving multi-resolution processing too. Parameters, go:

  • debug = 0; int in [0, 1] — write the distance transforms out to a 3D image 👀
  • maxlabel = 256; int in [2, 32000] — the maximum number of labels to consider
  • ref =(input, io) — the reference image. For supported file types see PLUGINS:2dimage/io
  • src =(input, io) — the study image. For supported file types see PLUGINS:2dimage/io
  • weight = 1; float — the weight of the cost function ⚖️

maskedimage 🎭

Generalized masked image similarity cost function, and yes, it also handles multi-resolution processing. Heads up though: the masks you feed it 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 😭. The reference mask and the transformed mask of the study image get combined with a binary AND. The actual similarity measure comes in as an extra parameter, and here's the menu:

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

🌈 PLUGINS: 2dimage/io 🌈

bmp 🖼️

BMP 2D-image input/output support, periodt. This plug-in reads and writes binary images and 8-bit gray scale images, and it gives you read-only support for 4-bit gray scale images. The color table gets straight-up 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, and it's giving parameters:

  • 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. The parameters:

  • cut = 0; float in [0, 40] — percentage of pixels to cut at 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 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 the reference image parzen hinstogram. For supported plug-ins see PLUGINS:1d/splinekernel

ncc 📐

normalized cross correlation with masking support.

  • (no parameters) — yeah, literally none, she's simple like that 🙈

ssd ➗

Sum of squared differences with masking.

  • (no parameters) 🫶

🎀 PLUGINS: 2dimage/transform 🎀

affine 📐

Affine transformation (six degrees of freedom), and the 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), with 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, 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), with 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, 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. Parameters:

  • 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), and the 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

vf 🧵

This plug-in implements a transformation that defines a translation for each point of the grid defining the domain of the transformation, and the 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 🌀

okay bestie so this one is all about the divcurl penalty on the transformation 💫 and here are the parameters it supports:

  • curl — curl = 1; float in [0, inf), which is the penalty weight on curl. yes, curl like the vibes, but numbers. 🌀
  • div — div = 1; float in [0, inf), the penalty weight on divergence. twin to the one above, fr fr.
  • norm — norm = 0; bool. set it to 1 if the penalty should be normalized with respect to the image size 📏 (read the note carefully, babes, it's a bool not a float).
  • weight — weight = 1; float in (0, inf), the weight of penalty energy. note the parens: it's a strictly positive range, no zero allowed, iykyk. 🔥

✨ PLUGINS: fastica/implementation ✨

the FastICA algorithm said "pick your fighter" and honestly? both slay 💖. here's the tea:

  • internal — this is the MIA implementation of the FastICA algorithm. homegrown, iconic, no cap. 👑

(no parameters)

  • itpp — this is the IT++ implementation of the FastICA algorithm. guest star energy, still ate. 🫶

(no parameters)

💅 PLUGINS: minimizer/singlecost 💅

Okay bestie, this is the other minimizer plugin family — the single-cost one. Let's get into it 🫶

🌸 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) — 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 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 ✨

Okay so picture this 💭 — you want to register the perfusion series given in segment.set by first using automatic ICA estimation to run the linear registration and then the PGT registration. You skip two images at the beginning and otherwise use the default parameters. And you store the result in registered.set. Iconic, no notes. 👑

mia-2dmyoicapgt   -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 warranty disclaimer is not a vibe, it's a warning — no warranty, periodt. 💀

That's a wrap on the whole page bestie 🦋 this one was from USER COMMANDS, version v2.4.7, living in the mia-2dmyoicapgt(1) section — go register something gorgeous and slay 💅✨🌸

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