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It includes filtered and simple back projection methods and evaluates reconstruction quality using various metrics.\n\n\u003cimg src=\"./resources/real_data.png\" width=400\u003e \u003cimg src=\"resources/synthetic_data.png\" width=400\u003e\n\n## Features\n\n- **Synthetic Data Generation**: Create CT phantoms with random shapes and noise.\n- **Radon Transform**: Compute sinograms.\n    - Optional custom implementation\n- **Image Reconstruction**: Perform filtered and simple back projections.\n    - Optional custom implementation\n- **Metrics Calculation**: Evaluate reconstruction quality using MSE, PSNR, and SSIM.\n- **Visualization**: Visualize images with Plotly and Matplgotlib.\n\n## Usage\n\n### Get Data\n\n```bash\npython3 main.py --generate # to generate\npython3 main.py --download # to download real data\n```\n\n### Process a Specific Sample\n\nTo process a sample by its ID:\n\n```bash\n# Process synthetic sample 2\npython main.py --process 2 --data-type synthetic\n\n# Process real samples\npython main.py --process 1_008 --data-type real\npython main.py --process 2b_001 --data-type real\n```\n\n## Dependencies\n\n- Python 3.8+\n- NumPy\n- SciPy\n- scikit-image\n- Plotly\n- Matplotlib\n- pydicom\n\n## DICOM data\n\n- https://www.visus.com/en/downloads/jivex-dicom-viewer.html\n    - Implemented","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fophiase%2Fradon-transform-study","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fophiase%2Fradon-transform-study","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fophiase%2Fradon-transform-study/lists"}