[WORKFLOW] Medical Resonance Imaging Simulation and Analysis

[TheCedarPrince]

Issue Description

Difficulty: Advanced
Time: 36 hours

Description:
This issue is to develop a workflow introducing medical imaging simulation and reconstruction workflows in Julia. This workflow will guide readers through simulating MRI data using KomaMRI.jl, reconstructing data with MRIReco.jl and BART, and enabling Julia–Python–C++ interoperability using BartIO.jl and PythonCall.jl.

The workflow will demonstrate how the Julia ecosystem supports an open, modular, and high-performance approach to computational MRI; from simulation of raw data to advanced image reconstruction pipelines. It will compare and integrate components written in Julia (MRIReco.jl, KomaMRI.jl) and external frameworks (BART in C/Python), showing how Julia acts as a unifying interface across languages and toolchains.

Requirements

• Introduce MRI simulation and reconstruction in the context of open-source computational imaging research.

• Explain KomaMRI.jl and its purpose: simulating MRI acquisitions by solving the Bloch equations with customizable sequences, phantoms, and scanner setups.

  • Demonstrate how to configure simulation parameters (Δt, Δt_rf, gpu, precision, Nblocks, etc.).
  • Show how to generate synthetic data for use in training or reconstruction.

• Demonstrate MRIReco.jl for image reconstruction.

  • Explain the modular structure of MRIReco.jl and its dependence on Julia packages (e.g., NFFT.jl, Wavelets.jl, LinearOperators.jl, RegularizedLeastSquares.jl).
  • Showcase compressed sensing reconstruction using ADMM.
  • Include examples with different k-space sampling patterns (Cartesian, spiral, radial).

• Introduce BART (Berkeley Advanced Reconstruction Toolbox) as a C/C++/Python-based MRI reconstruction framework.

  • Explain BART’s role in advanced image reconstruction research.
  • Include a minimal example of a bart phantom → bart nufft -i pipeline.

• Use BartIO.jl to interface BART from Julia.

  • Demonstrate reading/writing .cfl/.hdr files with read_cfl and write_cfl.
  • Show how to call BART commands (bart(1,"phantom -k -t", traj)) directly within Julia.
  • Include configuration examples using set_bart_path("/path/to/bart").

• Demonstrate interoperability with Python and C++ from Julia.

  • Use PythonCall.jl to import and call BART’s Python functions from Julia.
  • Describe when direct Julia wrappers (e.g., BartIO.jl) vs. foreign language interop may be preferred.
  • Illustrate Julia’s ability to orchestrate hybrid workflows that blend native and external tools.

• Provide an end-to-end workflow showing:

  • Simulation in KomaMRI.jl → Reconstruction in MRIReco.jl → BART reconstruction via BartIO.jl.
  • Optional integration with PythonCall.jl for hybrid experiments.
  • Visualization of reconstructed images in Julia (e.g., Makie.jl, Images.jl).

Expected Outcomes

1. A workflow introducing medical imaging simulation and reconstruction workflows.
2. Code examples showing data generation (KomaMRI), image reconstruction (MRIReco, BART), and Julia–C++/Python interoperability (BartIO.jl, PythonCall.jl).
3. Demonstration of Julia as a unifying environment for imaging research.
4. A reproducible example pipeline integrating multiple MRI tools.
5. Reference and comparison of open-source MRI ecosystems across languages.
6. Visual examples illustrating simulated k-space data and reconstructed MRI images.

Notes

Reference Materials

• MRIReco.jl GitHub (https://github.com/MagneticResonanceImaging/MRIReco.jl)
• KomaMRI.jl GitHub (https://github.com/KomaMRI/KomaMRI.jl)
• BartIO.jl GitHub (https://github.com/MagneticResonanceImaging/BartIO.jl)
• BART Toolbox (https://mrirecon.github.io/bart/)
• PythonCall.jl Documentation (https://cjdoris.github.io/PythonCall.jl/stable/)
• MRIReco.jl Paper: Uecker, M. et al. (2015). Software for fast MRI reconstruction using non-uniform FFTs and compressed sensing.
• KomaMRI.jl Documentation: Describes Bloch simulation parameters, simulate API, and sim_params configuration.
• BART Reference: Uecker, M. et al. (2015). Berkeley Advanced Reconstruction Toolbox.