🚀 Introduction

Welcome to the official Ultralytics repository dedicated to analyzing gamma scatter interactions on Lutetium–Yttrium Oxyorthosilicate (LYSO) crystals. This project leverages Ultralytics' expertise in data-driven research to advance understanding in particle physics and imaging technology.

📜 Description

Our goal is to push the boundaries of particle detection and imaging. This repository, Ultralytics PETSYS, focuses on the detailed analysis of gamma scatter phenomena observed on LYSO crystals coupled with Silicon Photomultipliers (SiPMs). The dataset originates from PETSYS electronics in Lisbon, Portugal, collected in 2017. It forms the basis for our algorithms designed to perform energy calibration and assess the timing resolution of these sophisticated detection systems, crucial for applications like Positron Emission Tomography (PET).

📦 Requirements

To explore the intricacies of scintillation and photo-detection with our tools, you'll need specific software and toolboxes. Ensure you have MATLAB R2018a or newer installed. Additionally, certain MATLAB toolboxes focused on statistical analysis and signal processing are required.

Get started by:

1. Cloning our common functions repository:

   git clone https://github.com/ultralytics/functions-matlab

2. Adding this repository and the common functions repository to your MATLAB environment:

   addpath(genpath('/path/to/petsys'))
   addpath(genpath('/path/to/functions-matlab'))

3. Ensuring the following MATLAB toolboxes are installed:

   • Statistics and Machine Learning Toolbox
   • Signal Processing Toolbox

4. Optionally installing the Python dependencies used by test_singles.py:

   python -m pip install matplotlib numpy pandas scipy seaborn

With these prerequisites met, you're ready for high-fidelity analysis!

🏃‍♀️ Running the Code

Initiating the analysis is simple. Execute the following command in your MATLAB console:

testSingles  % This script starts the analysis of the gamma scatter dataset

You can also run the Python port from the repository root:

python test_singles.py

The scripts generate graphical outputs and insights, including visualizations like the one shown below:

📑 Citation

If you find this dataset or our analysis tools beneficial for your research, please acknowledge our work by citing the DOI provided:

💡 Contribute

Contributions from the community are highly encouraged! Whether it's fixing bugs, proposing new features, or enhancing documentation, your input helps us improve. Please see our Contributing Guide for details on how to get started. We'd also appreciate hearing about your experience using Ultralytics products through our Survey. A heartfelt 🙏 thank you to all our contributors!

📄 License

Ultralytics provides two licensing options to accommodate diverse needs:

• AGPL-3.0 License: An OSI-approved open-source license ideal for students and enthusiasts keen on contributing to open projects. See the LICENSE file for details.
• Enterprise License: Designed for commercial applications, this license permits the integration of Ultralytics software and AI models into commercial products and services without the open-source stipulations of AGPL-3.0. For commercial use inquiries, please contact us via Ultralytics Licensing.

📮 Contact

For bug reports, feature suggestions, and contributions, please visit GitHub Issues. For broader questions and discussions related to this project or other Ultralytics initiatives, join our active community on Discord!