mia-2dmyop...c-nonrigid(1)

📦
version 2.4.7 · 3 versions
, in debian bookworm, ubuntu noble

🌸 NAME 🌸

okay bestie, this is mia-2dmyoperiodic-nonrigid — the manual header calls it mia-2dmyop...c-nonrigid(1), but we're here now. mia-2dmyoperiodic-nonrigid — Run a registration of a series of 2D images.

✨ SYNOPSIS ✨

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

🦋 DESCRIPTION 🦋

mia-2dmyoperiodic-nonrigid is the main character that runs the non-rigid registration of a perfusion image series, preferably one acquired while the patient is breathing freely. The registration algorithm implementes (their spelling, and it's iconic 💅) this paper:

G. Wollny, M-J Ledesma-Cabryo, P.Kellman, and A.Santos, "Exploiting Quasiperiodicity in Motion Correction of Free-Breathing," IEEE Transactions on Medical Imaging, 29(8), 2010.

🎀 OPTIONS 🎀

💾 File-IO 💾

  • -i, --in-file=(required, input); string — the input perfusion data set. It's required, bestie.
  • -o, --out-file=(required, output); string — the output perfusion data set. Also required.
  • -r, --registered=reg — file name base for registered fiels (yes, fiels, I'm keeping the original spelling 🫶).
  • --save-references — save synthetic references to files named refXXXX.v.

🧠 Preconditions 🧠

  • -k, --skip=0 — skip images at the begin of the series.
  • --max-candidates=20 — maximum number of candidates for the global reference image.
  • -S, --cost-series=image:cost=[ngf:eval=ds] — Const function to use for the analysis of the series. (Yes, Const, not Cost — I'm just the messenger 🎀.) For supported plugins see PLUGINS:2dimage/fullcost.
  • --ref-idx= — save reference index number to this file.
  • -R, --global-reference=-1 — save reference index number to this file. (Same description as --ref-idx=, no cap. That's what it says.)
  • -D, --max-subset-delta=0 — Maximum delta between two elements of the prealigned subset.

🎀 Registration 🎀

  • -O, --optimizer=gsl:opt=gd,step=0.01 — Optimizer used for minimization. For supported plugins see PLUGINS:minimizer/singlecost.
  • --refiner= — optimizer used for additional minimization. For supported plugins see PLUGINS:minimizer/singlecost.
  • -l, --mr-levels=3 — multi-resolution levels.
  • -f, --transForm=spline:rate=16,penalty=[divcurl:weight=0.01] — transformation type. For supported plugins see PLUGINS:2dimage/transform.
  • -1, --cost-subset=image:cost=[ngf:eval=ds] — Cost function for registration during the subset registration. For supported plugins see PLUGINS:2dimage/fullcost.
  • -2, --cost-final=image:cost=ssd — Cost function for registration during the final registration. For supported plugins see PLUGINS:2dimage/fullcost.

🆘 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 🔌

okay so these are the spline interpolation boundary conditions, and they're giving "what happens at the edge" energy.

mirror

Spline interpolation boundary conditions that mirror on the boundary. It's basically reflecting at the boundary.

  • (no parameters)

repeat

Spline interpolation boundary conditions that repeats the value at the boundary. Same value, just repeated.

  • (no parameters)

zero

Spline interpolation boundary conditions that assumes zero for values outside. Outside? Zero. Periodt.

  • (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

okay bestie, this is the whole squad of image readers/writers. every single one, no skipping, let's go 💖

  • bmp — BMP 2D-image input/output support. This plug-in handles reading and writing of binary images and 8-bit gray scale images. Reading 4-bit gray scale images is read-only — like, you can look but don't touch. The color table gets ignored and the pixel values are just taken as literal gray scale values. 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. yes, output. that's the whole vibe.
    • 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

these are the cost functions but make it masked. like, they only care about some pixels. main character energy only.

  • 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. (so the window is 2*w+1 vibes, you get it)
  • 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. bye 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) — literally none. unbothered. 💅
  • ssd — Sum of squared differences with masking.
    • (no parameters) — same, no parameters, iconic.

🌀 PLUGINS: 2dimage/transform

the transformation plug-ins!! this is where the actual wiggling and warping happens bestie 🫶

  • 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

penalty terms for the spline transform!! it's giving "behave yourself, transformation" 😌

  • 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.

okay that's this chunk, but bestie we are NOT done, keep scrolling 👑

PLUGINS: minimizer/singlecost 💅

Okay bestie, this is the squad of minimizers — the little algorithms that actually go hunting for the best answer. Four of them, all living under the singlecost umbrella. Pick your fighter. 🥊

gdas 🔥

Gradient descent, but make it ✨automatic✨ — it corrects the step size on its own. Params:

  • ftolr = 0; double in [0, inf) — stop once the relative change of the criterion dips below this.
  • max-step = 2; double in (0, inf) — the biggest absolute step size it'll ever take.
  • maxiter = 200; uint in [1, inf) — stopping criterion: the maximum number of iterations, periodt.
  • min-step = 0.1; double in (0, inf) — the smallest absolute step size, no going lower than this.
  • xtola = 0.01; double in [0, inf) — stop when the inf-norm of the change applied to x drops below this value.

gdsq 💫

Gradient descent with quadratic step estimation — it's giving main character energy, doing math to guess its own step size:

  • ftolr = 0; double in [0, inf) — stop if the relative change of the criterion is below this.
  • 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, for when the fancy estimation needs a backup plan.
  • step = 0.1; double in (0, inf) — initial step size.
  • xtola = 0; double in [0, inf) — stop if the inf-norm of the x-update is below this value.

gsl 🌈

This one's an optimizer plugin built on the multimin optimizers from the GNU Scientific Library (GSL), over at https://www.gnu.org/software/gsl/. Obsessed, honestly. Params:

  • eps = 0.01; double in (0, inf) — for gradient based optimizers: stop when |grad| < eps; for simplex: stop when the 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. For a full description of the optimizers, go peek at http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms — that's the wiki, bestie, it has the lore. Params:

  • 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 — the 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 ✨

Real scenario, bestie: register the perfusion series that's sitting in segment.set. Skip the first two images, use a spline transformation with a knot rate of 16 pixels, and penalize the transformation with divcurl at weight 5. Result lands in registered.set. Slay. 💖

mia-2dmyoperiodic-nonrigid   -i segment.set -o registered.set -k 2 -d 5
       -f spline:rate=16,penalty=[divcurl:weight=5]

AUTHOR(s) 🫶

Gert Wollny — the icon, the legend, the moment. 👑

okay bestie, legal era 💅 this part is short but it matters, so pay attention.

This software is Copyright (c) 1999‐2015 Leipzig, Germany and Madrid, Spain.

It comes with ABSOLUTELY NO WARRANTY — like, none, zero, zilch, the vibes are "you're on your own" 😭 — 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.

no cap, "ABSOLUTELY NO WARRANTY" is doing a lot of heavy lifting in that sentence. it's giving "good luck bestie" fr fr 🙈

and that's the whole page, babes 🎀 source is USER COMMANDS, version v2.4.7 — you ate that read, now go run your nonrigid registration and slay ✨🦋💖

📦 where this page comes from