KomaMRI.jl

KomaMRI.jl is a Julia package for highly efficient ⚡ MRI simulations. KomaMRI was built from the ground up to be: easy to use, extensible, cross-platform, and powered by open-source community standards.

Table of Contents

• News
• Installation
• First run
• How to Contribute
• How to Cite
• Tested compatibility
• PkgEval

News

• (23 Jul 2025) KomaMRI.jl was presented at JuliaCon 2025. Video available here, and slides here.
• (16 Jun 2025) KomaMRI v0.9.1 has received a perfect score in the Open Source Imaging Initiative (OSI²) CAB Review 🥳! Read the full review report here.
• (1 Oct 2024) KomaMRI v0.9: device-agnostic simulations, improved performance (4-5x faster and 80x less memory), distributed simulations, GPU benchmarking, mix-and-match motion definitions, improved dynamic phantom plotting, and a new phantom file format!
• (29 Aug 2024) Our first GSoC student, Ryan Kierulf, presented his fantastic work at the JuliaHealth monthly meeting 🥳! (presentation available here) More info in the docs: GPU Parallelization, Distributed Simulations and Ryan’s JuliaHealth blog
• (7 Dec 2023) Koma was present in MRI Together 😼. The talk is available here. Also, I uploaded the promised educational example.
• (17 Nov 2023) Pretty excited of being part of ISMRM Pulseq’s virtual meeting. The slides available here.
• (27 Jul 2023) I gave a talk at MIT 😄 for JuliaCon 2023! A video of the presentation can be seen here.
• (29 Jun 2023) KomaMRI.jl’s paper was chosen as a July editor’s pick in MRM 🥳!
• (6 Mar 2023) Paper published in MRM 😃!
• (8 Dec 2022) KomaMRI v0.7: improved performance (5x faster), type stability, extensibility, and more!
• (17 May 2022) ISMRM 2022 digital poster presented in London, UK. Recording here!. Name change MRIsim.jl -> KomaMRI.jl.
• (Aug 2020) Prehistoric version of Koma, MRIsim, presented as an ISMRM 2020 digital poster (virtual conference).

☰ Roadmap

v1.0:

• Phantom and Sequence data types,
• Spin precession in gradient-only blocks (simulation optimization),
• GPU acceleration using CUDA.jl,
• RF excitation,
• GPU accelaration of RF excitation,
• Scanner data-type: , etc.,
• Pulseq IO,
• Signal “Raw Output” dictionary (ISMRMRD),
• MRIReco.jl for the reconstruciton,
• Documentation,
• Auxiliary Pulseq functions,
• Coil sensitivities,
• Cardiac phantoms and triggers.
• decay,

Next:

• Diffusion models with Laplacian Eigen Functions,
• Magnetic susceptibility,
• Use PackageCompiler.jl to build a ditributable core or app.

Installation

To install, just type ] add KomaMRI in the Julia REPL or copy-paste the following into the Julia REPL:

pkg> add KomaMRI
pkg> add CUDA     # Optional: Install desired GPU backend (CUDA, AMDGPU, Metal, or oneAPI (experimental))
pkg> app add KomaMRI # Optional: Install the koma app (Julia 1.12+)

For more information about installation instructions, refer to the section Getting Started of the documentation.

First run

KomaMRI.jl features a convenient GUI with predefined simulation inputs (i.e. Sequence, Phantom, and Scanner). To launch the GUI, use the following command:

using KomaMRI
using CUDA        # Optional: Load GPU backend (default: CPU)
KomaUI()

Press the button that says “Simulate!” to do your first simulation :). Then, a notification will emerge telling you that the simulation was successful. In this notification, you can either select to (1) see the Raw Data or (2) to proceed with the reconstruction.

How to Contribute

KomaMRI exists thanks to all our contributors:

Want to be highlighted here? We welcome contributions from the community! If you’re interested in contributing, please read “CONTRIBUTING” for details on how to get started.

How to Cite

If you use this package, please cite our papers.

Plain Text:

Castillo-Passi, C, Coronado, R, Varela-Mattatall, G, Alberola-López, C, Botnar, R, Irarrazaval, P. KomaMRI.jl: An open-source framework for general MRI simulations with GPU acceleration. Magn Reson Med. 2023; 90, no. 1: 1-14. doi: 10.1002/mrm.29635

Villacorta-Aylagas, P, Castillo-Passi, C, Kierulf, R.A, Menchón-Lara, R.M, Rodríguez-Galván, J.R, Sierra-Pallares, J.B, Irarrazaval, P, Alberola-López, C. Versatile and Highly Efficient MRI Simulation of Arbitrary Motion in KomaMRI. Magn Reson Med. 2026; 95, no. 3: 1791–1803. doi: 10.1002/mrm.70145.

BibTex:

@article{https://doi.org/10.1002/mrm.29635,
    author = {Castillo-Passi, Carlos and Coronado, Ronal and Varela-Mattatall, Gabriel and Alberola-López, Carlos and Botnar, René and Irarrazaval, Pablo},
    title = {KomaMRI.jl: An open-source framework for general MRI simulations with GPU acceleration},
    journal = {Magnetic Resonance in Medicine},
    volume = {90},
    number = {1},
    pages = {329-342},
    keywords = {Bloch equations, GPU, GUI, Julia, open source, simulation},
    doi = {https://doi.org/10.1002/mrm.29635},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.29635},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.29635},
    year = {2023}
}

@article{https://doi.org/10.1002/mrm.70145,
    author = {Villacorta-Aylagas, Pablo and Castillo-Passi, Carlos Andrés and Kierulf, Ryan Anders and Menchón-Lara, Rosa María and Rodríguez-Galván, Justino R. and Sierra-Pallares, José Benito and Irarrazaval, Pablo and Alberola-López, Carlos},
    title = {Versatile and Highly Efficient MRI Simulation of Arbitrary Motion in KomaMRI},
    journal = {Magnetic Resonance in Medicine},
    volume = {95},
    number = {3},
    pages = {1791-1803},
    keywords = {motion, MRI simulation, open-source, performance},
    doi = {https://doi.org/10.1002/mrm.70145},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.70145},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.70145},
    year = {2026}
}

Tested compatibility

CUDA, AMDGPU, and Metal are tested on Linux (besides Apple silicon) using the latest stable release, Julia 1 (stable), and Julia 1.10 (compat). oneAPI support is experimental and is not included in the status badges below.

Single-threaded compatibility is tested in all major operating systems (OS).

If you see any problem with this information, please let us know in a GitHub issue.

PkgEval

PkgEval is Julia’s automated package evaluation system. It periodically tests registered packages against Julia versions to catch ecosystem regressions before they reach users.

The umbrella KomaMRI package is excluded here because it opens UI paths that PkgEval cannot display. The subpackages below are the useful PkgEval signal.