Welcome to the Analytical Tomographic Image Reconstruction repository! This collection of Python code provides implementations for various analytical tomographic image reconstruction algorithms. These algorithms play a crucial role in transforming projection data into detailed images, making them valuable tools in medical imaging, materials science, and more. Included are Fourier Gridding, Backproject Filter, Filtered Backprojection and Convolve Backproject
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backprojection.py
- Implementation of the Backprojection algorithm.
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data_generator.py
- Generates 2D data for testing purposes.
- Options include loading an image, using the Shepp-Logan phantom, or generating a simple Disk object.
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filters.py
- Implementation of various filters used in tomographic image reconstruction.
- Includes filters for Backproject Filter and Filter Backprojection algorithms.
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fourier_gridding.py
- Performs radial-to-cartesian interpolation on a 2D Fourier Transform.
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fourier_transform.py
- Implementation of the Fourier Transform.
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radon_transform.py
- Implementation of the Radon Transform.
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tutorials.ipynb
- Jupyter notebook demonstrating the usage of all reconstruction methods with examples and visualizations.
To get started, explore the provided tutorials.ipynb notebook for comprehensive examples and visualizations of each reconstruction method. You can experiment with different data sources and filters to see how these algorithms perform under various conditions.
We welcome contributions to enhance and expand this repository. If you have improvements, additional algorithms, or new features to suggest, please feel free to open an issue or submit a pull request.
Special thanks to me for writing this and Prof. Lasser for the wonderful Seminar course
This code is released under the MIT License. Feel free to use, modify, and distribute the code in accordance with the terms of the license.
Happy reconstructing! 🌐🖼️