OpenRBYR is an open-source Python package for simulating Ray-by-Ray Computed Tomography (RBYRCT) scan paths. This toolkit enables researchers to explore and develop CT imaging methodologies with a focus on precision and low-dose imaging techniques.

• Simulated ray paths & scan trajectories: Model different CT setups and beam configurations.
• Monte Carlo simulation integration: Example setups for TOPAS and Geant4.
• Basic MART-like reconstruction: Implementations to test reconstruction methods.
• Synthetic CT phantoms: Sample datasets for algorithm validation.

# Clone the repository
git clone https://github.com/HussainAther/OpenRBYR.git
cd OpenRBYR

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Here’s an example of how to simulate a simple CT scan setup:

from openrbyr import ray_simulation

# Simulate rays for a CT setup
rays = ray_simulation.simulate_rays(num_rays=100, detector_distance=50)
ray_simulation.visualize_rays(rays)

OpenRBYR/
│── examples/            # Sample simulations & reconstructions
│── notebooks/           # Jupyter notebooks for tutorials
│── openrbyr/            # Main package source code
│   │── __init__.py
│   │── ray_simulation.py # Core ray-path simulator
│   │── monte_carlo.py   # Monte Carlo simulation interface
│   │── reconstruction.py # Basic MART-like reconstruction
│── tests/               # Unit tests
│── synthetic_phantoms/  # Sample CT phantoms
│── docs/                # Documentation
│── README.md            # Main project description
│── INSTALL.md           # Installation instructions
│── CONTRIBUTING.md      # Contribution guidelines
│── LICENSE              # License file
│── .gitignore           # Ignore unnecessary files

We welcome contributions from the community! To get started:

1. Fork the repository.
2. Create a new branch (feature-name).
3. Commit and push your changes.
4. Submit a pull request.

✅ Implement GAN-based CT Denoising (AI-powered artifact reduction)
✅ Support cloud-based Monte Carlo simulations
✅ Integrate Web Visualization Tools (e.g., Three.js for interactive 3D rendering)
✅ Develop AI-assisted inpainting for limited-angle CT
✅ Create an API for real-time CT reconstruction

See CONTRIBUTING.md for more details.

This project is licensed under the MIT License. See LICENSE for details.

Special thanks to Richard Gordon and the OREL research team for their contributions to RBYRCT technology.