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

	Features:
	- Fast simulations with CPU/GPU parallelization 🏃💨 - Extensible, so anyone can include new features 🆙 - Supports community-standards [🤝](## "Pulseq and ISMRMRD") - Interactive visualizations using PlotlyJS.jl 📲 - Cross-platform (Windows, Mac & Linux) 🌐 - Friendly GUI (using web technologies) 😌 - Compatible with modern notebooks [🎈](## "Pluto and Jupyter") - Flexible API for advanced users 👨‍💻

Table of Contents

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

News

• (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) KomaNYU.jl's paper was chosen as a July editor's pick in MRM 🥳!
• (6 Mar 2023) Paper published in MRM 😃!
• (8 Dec 2022) KomaNYU 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 -> KomaNYU.jl.
• (Aug 2020) Prehistoric version of Koma, MRIsim, presented as an ISMRM 2020 digital poster (virtual conference).

☰ Roadmap

v1.0: - [x] Phantom and Sequence data types, - [x] Spin precession in gradient-only blocks (simulation optimization), - [x] GPU acceleration using CUDA.jl, - [x] RF excitation, - [x] GPU accelaration of RF excitation, - [x] Scanner data-type: , etc., - [x] [Pulseq](https://github.com/imr-framework/pypulseq) IO, - [x] Signal "Raw Output" dictionary ([ISMRMRD](https://ismrmrd.github.io/)), - [x] [MRIReco.jl](https://magneticresonanceimaging.github.io/MRIReco.jl/latest/) for the reconstruciton, - [ ] Documentation, - [ ] [Auxiliary Pulseq functions](https://github.com/imr-framework/pypulseq/tree/master/pypulseq), - [ ] Coil sensitivities, - [ ] Cardiac phantoms and triggers. - [ ] decay, Next: - [ ] Diffusion models with Laplacian Eigen Functions, - [ ] Magnetic susceptibility, - [ ] Use [PackageCompiler.jl](https://julialang.github.io/PackageCompiler.jl/dev/apps.html) to build a ditributable core or app.

Installation

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

```julia pkg> add KomaNYU pkg> add CUDA # Optional: Install desired GPU backend (CUDA, AMDGPU, Metal, or oneAPI)

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

First run

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

julia using KomaNYU 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.

[!IMPORTANT] Starting from KomaNYU v0.9 we are using package extensions to deal with GPU dependencies, meaning that to run simulations on the GPU, installing (add CUDA/AMDGPU/Metal/oneAPI) and loading (using CUDA/AMDGPU/Metal/oneAPI) the desired backend will be necessary (see GPU Parallelization and Tested compatibility).

How to Contribute

KomaNYU 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 our Contribution Guidelines for details on how to get started.

How to Cite

If you use this package, please cite our paper.

Plain Text:

Castillo-Passi, C, Coronado, R, Varela-Mattatall, G, Alberola-López, C, Botnar, R, Irarrazaval, P. KomaNYU.jl: An open-source framework for general MRI simulations with GPU acceleration. Magn Reson Med. 2023; 1- 14. doi: 10.1002/mrm.29635

BibTex: 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 = {KomaNYU.jl: An open-source framework for general MRI simulations with GPU acceleration}, journal = {Magnetic Resonance in Medicine}, 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}, }

Tested compatibility

All parallel backends are tested on Linux (besides Apple silicon) using the latest stable release, Julia 1 (stable), and Julia 1.9 (compat).

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.