xmed-lab/CvG-Diff
[MICCAI 2025 Spotlight] Cross-view Generalized Diffusion Model for Sparse-view CT Reconstruction
This project helps medical imaging professionals create high-quality CT scans using significantly fewer X-ray projections. By taking limited, 'sparse-view' CT data, it outputs detailed, artifact-free reconstructed CT images. It is designed for radiologists, imaging scientists, and medical physicists aiming to reduce radiation exposure without compromising diagnostic image quality.
Use this if you need to reconstruct clear, high-resolution CT images from a very limited number of X-ray views, improving patient safety and imaging efficiency.
Not ideal if you already have dense, full-view CT datasets and are not concerned with reducing projection numbers or radiation dose.
Stars
10
Forks
—
Language
Python
License
MIT
Category
Last pushed
Oct 18, 2025
Commits (30d)
0
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