ROCI: Repository of Computational Imaging

ROCI is a collection of minimalistic, high-quality, and self-contained PyTorch (re-)implementations of algorithms used in computational imaging.

In terms of applications, ROCI focuses on forward and inverse problems appearing in computational MRI (reconstruction, quantification, synthesis, physics simulation). In terms of techniques, it includes classical signal processing algorithms alongside SoTA representation learning and generative modeling methods. All algorithms are written in PyTorch. This means autograd compatibility and GPU-accelerated compute.

Code Organization

All algorithms are stored in the algorithms directory. The directory structure is simple: algorithm = python_file + demo_notebook + readme.

algorithms
    |
    |- algo_1
    |   |- algo_1.py
    |   |- Demo.ipynb
    |   |- README.md
    |
    |- algo_2
    |   |- algo_2.py
    |   |- Demo.ipynb
    |   |- README.md
    ...

ROCI also provides tiny versions of some algorithms applied on miniature toy problems, e.g. generative modeling of toy distributions in 2D space. These can be found in the tiny directory.

ROCI represents a loose collection of algorithms, not a coherent Python library. Such a python library is under development as a separate project, temporarily being housed in this repo under rocilib.

Available Algorithms

MRI reconstruction:

• SENSE parallel-imaging reconstruction
• CG-SENSE parallel-imaging reconstruction
• Compressed sensing reconstruction
• GRAPPA parallel-imaging reconstruction
• ESPIRiT parallel-imaging
• MoDL unrolled network
• Implicit neural representations for differentiable uncalibrated imaging
• Deep image prior

MRI forward physics simulation:

• Bloch signal simulation
• Extended phase graphs (EPG) method

MRI quantification:

• Physics-informed MLP for MRF parameter mapping

Coming Soon

Inversion:

• Equivariant imaging
• Equivariant splitting
• Double blind imaging with generative modeling
• Compressed-sensing with generative modeling (CSGM)
• PnP-CoSMo: Plug-and-play reconstruction based on content/style modeling
• PnP-CM
• PnP-Flow
• NullFlow: One-Step Generative Reconstruction
• noise2noise denoising
• BM3D denoising
• Motion-corrected MRI with DISORDER
• Magnetic resonance spin tomography in time-domain (MR-STAT)
• Data-driven discovery of mechanical models directly from MRI spectral data
• Neural Inverse Rendering from Propagating Light

Representations:

• Gaussian representations
• Structured representations using flow-based models
• Non-linear ICA for Principled Disentanglement in Unsupervised Deep Learning
• From Pixels to Components: Eigenvector Masking for Visual Representation Learning
• Tensorial radiance fields

Forward physics modeling:

• Unified Bloch and EPG method
• Phase distribution graphs (PDG) method for MR signal simulation
• Quantum mechanical MRI simulations
• UltimateSynth: MRI Physics for Pan-Contrast AI
• Neural surrogates based on PINNs
• Neural surrogates based on Fourier neural operators

Citation

If you use any code from this repository, please cite it as:

@software{Rao_ROCI_2026,
  author = {Rao, Chinmay},
  month = feb,
  title = {{Repository of Computational Imaging (ROCI)}},
  version = {0.1.1},
  year = {2026}
}