mia-2dgroundtruthreg(1)

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

✨ NAME ✨

ok so bestie, mia-2dgroundtruthreg — that's the manual name, and it lives in the General Commands Manual section — it's literally Registration of a series of 2D images 💖

🎀 SYNOPSIS 🎀

mia-2dgroundtruthreg -i <in-file> -o <out-file> -A <alpha> -B <beta> -R
<rho_thresh> [options]

💅 DESCRIPTION 💅

mia-2dgroundtruthreg is the program that implements non-linear registration based on Pseudo Ground Thruth for motion compensation of series of myocardial perfusion images 🫀✨ — exactly as described in:

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.

ngl this next part matters: for this nonlinear motion correction a preceding linear registration step is usually required. like, do the linear one first, okay? don't skip it 😭

🌸 OPTIONS 🌸

📁 File-IO 📁

  • -i, --in-file=(input, required); string — the input perfusion data set. required, no cap 💅
  • -o, --out-file=(required, output); string — the output perfusion data set. also required, also non-negotiable 👑
  • -r, --registered=reg — file name base for registered files

🦋 Preconditions 🦋

  • -s, --skip=2 — skip images at beginning of series (default is 2, she's a lil impatient, we respect it)
  • -P, --passes=4 — number of registration passes (default 4, iconic)

💀 Pseudo-Ground-Thruth 💀

  • -A, --alpha=(required); double — spacial neighborhood penalty weight
  • -B, --beta=(required); double — temporal second derivative penalty weight
  • -R, --rho_thresh=(required); double — correlation threshold for neighborhood analysis

heads up: all three of these are required. the program will simply not be doing the vibe check without them fr fr 🙈

🔥 Registration 🔥

  • -O, --optimizer=gsl:opt=gd,step=0.1 — Optimizer used for minimization. For supported plugins see PLUGINS:minimizer/singlecost
  • -p, --interpolator=bspline:d=3 — image interpolator kernel. For supported plugins see PLUGINS:1d/splinekernel
  • -l, --mr-levels=3 — multi-resolution levels
  • -d, --divcurl=20 — divcurl regularization weight
  • --divcurl-divider=4 — divcurl weight scaling with each new pass
  • -a, --start-c-rate=32 — start coefficinet rate in spines, gets divided by --c-rate-divider with every pass
  • --c-rate-divider=4 — cofficient rate divider for each pass
  • -w, --imageweight=1 — image cost weight

🧚 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).

so basically if you don't know what to put, leave it at -1 and let her figure it out, she's got main character energy 💅

🔌 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.

✨ okay bestie that's the first chunk, more is coming, stay tuned ✨

🧮 minimizer/singlecost 🧮

Okay bestie, this is the section where you pick how the minimizer does its little optimization dance. Every one of these is a plugin, and every one has its own parameters, so buckle up 💖

✨ gdas ✨

Gradient descent with automatic step size correction. She's simple, she's classic, she works. Here's what she takes:

  • ftolr = 0 — double in [0, inf). Stop if the relative change of the criterion is below.
  • max-step = 2 — double in (0, inf). Maximal absolute step size.
  • maxiter = 200 — uint in [1, inf). Stopping criterion: the maximum number of iterations.
  • min-step = 0.1 — double in (0, inf). Minimal absolute step size.
  • xtola = 0.01 — double in [0, inf). Stop if the inf-norm of the change applied to x is below this value.

🌸 gdsq 🌸

Gradient descent but make it ✨quadratic✨ — this one estimates the step with a quadratic fit instead of just vibing. Parameters:

  • 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 👑

This one's an optimizer plugin built on the multimin optimizers of the GNU Scientific Library (GSL) — go peep https://www.gnu.org/software/gsl/. Parameters, bestie:

  • 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. For a description of the optimizers please see http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms. Ngl this list is long, but we do not skip a single one 💅

  • 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 here's the moment you've been waiting for — register the perfusion series given by images imageXXXX.exr by using Pseudo Ground Truth estimation. Skip two images at the beginning and otherwise use the default parameters. Store the result images to regXXXX.exr. Slay.

mia-2dgroundtruthreg -i imageXXXX.exr -o regXXXX.exr -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, read that warranty line again. There is none. None whatsoever. 😭

And that's the whole page, bestie — this was mia-2dgroundtruthreg(1), straight out of the USER COMMANDS section, version v2.4.7, dated as per the original page footer. Go forth and register those perfusion series, you absolute icon 👑✨🦋

📦 where this page comes from