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  • Homepage
  • User guide
    • Prerequisites and supported platforms
    • Installation
    • From BART and sigpy
    • Reporting issues
    • Questions and proposals
    • Security
  • Developer guide
    • Development prerequisites
    • Editable and source installation
    • Repository layout
    • Development workflow
    • The BART fork
    • Pre-commit hooks
    • Coding style
    • Documentation
    • Terminology and conventions
    • Pull requests
    • Code of conduct
  • Explanation
    • Interfaces and execution
    • Data layout and conventions
    • Inverse problems and their solvers
    • The MRI encoding operator
    • Non-Cartesian sampling
    • Nonlinear forward models
    • Differentiation through reconstruction
    • Learned reconstruction
  • Examples
    • Basics
      • Tensors and commands
      • From k-space to image
    • Parallel imaging
      • Coil sensitivity calibration
      • Nonlinear inversion
      • Noise prewhitening
    • Regularization
      • Regularized reconstruction
      • Operators and solvers
    • Non-Cartesian imaging
      • Trajectories and transforms
      • Radial SENSE reconstruction
      • Dynamic golden-angle radial MRI
    • Model-based reconstruction
      • Subspace-constrained T1 mapping
      • Parameter maps straight from k-space
      • Parameter maps from scanner images
    • Learned regularization
      • Plug-and-play denoisers
      • MoDL, on BART’s ADMM
      • Networks for complex volumes
      • Staged training of an unrolled network
      • Training without a reference
      • Annealed plug-and-play
      • Uncertainty estimation
    • Tours
      • Readout oversampling and apodization
      • EPI Nyquist ghost and ramp sampling
      • Receive bias field
      • Gradient nonlinearity
      • Off-resonance correction of spiral imaging
      • Rigid head motion from navigators
      • Susceptibility distortion in EPI
  • API reference
    • Array functions and settings
    • BART commands as functions
    • Linear operators
    • Nonlinear operators
    • Optimization
    • Regularization and denoising
    • Reconstruction pipelines
    • Learning
    • Interoperability
    • File I/O
    • Command line
  • Miscellaneous
    • License and third-party notices
    • Related projects
    • Contributors and citation
  • Repository
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  • .md

Related projects

Contents

  • Components of bartorch
  • Other reconstruction frameworks

Related projects#

Components of bartorch#

Project

Relationship

BART

The reconstruction toolbox bartorch embeds, developed at codeberg.org/mrirecon/bart and pinned as the external/bart submodule from the downstream fork pulserver/bart

PyTorch

Tensors, devices and automatic differentiation of every interface

FINUFFT and cuFINUFFT

Non-uniform fast Fourier transforms substituted for BART’s gridding on the host and on CUDA devices

MRI-NUFFT

Fits the time-segmentation coefficients of bartorch.linop.FieldCorrected()

TorchSim

Signal simulation for the signal models of bartorch.nlop

DeepInverse

Target of bartorch.interop.to_deepinv(), and a source of denoisers for ImplicitPrior

SimpleITK

Resampling for bias field and gradient nonlinearity correction, and registration of navigator planes, in bartorch.tools

PyHySCO

Susceptibility distortion correction behind bartorch.tools.correct_susceptibility()

Other reconstruction frameworks#

Project

Relationship

SigPy

Python operators, proximal operators and MRI reconstruction apps, implemented in NumPy and CuPy; an independent implementation for checking conventions

MRpro

MRI reconstruction implemented in PyTorch itself, with its own operators and data handling

MRIReco.jl

MRI reconstruction in Julia

Pyxu

General computational-imaging framework; its treatment of forward operators, functionals and proximal algorithms is the conceptual reference for Inverse problems and their solvers

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License and third-party notices

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Contributors and citation

Contents
  • Components of bartorch
  • Other reconstruction frameworks

By bartorch contributors

© Copyright 2024–2026, bartorch contributors.