Hexagonal Basis Projectors for Model-Based Iterative CT Reconstruction
We develop constant and linear hexagonal projectors for two-dimensional fan-beam CT using box spline calculus.
ProofPaper ↗
Key points
- Image representation affects both the forward model and quantitative feature recovery in model-based iterative CT reconstruction.
- For isotropically bandlimited signals, the hexagonal lattice is the most sampling-efficient lattice in two dimensions, with greater directional isotropy and a larger alias-free bandwidth than the Cartesian grid at equal density.
- We derive analytic ray footprints and effective detector-blur expressions and implement efficient GPU forward and backprojection.
- These results establish a practical hexagonal representation for quantitative CT.
Sources (1)
- [1]Hexagonal Basis Projectors for Model-Based Iterative CT ReconstructionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 01:27 AM
We develop constant and linear hexagonal projectors for two-dimensional fan-beam CT using box spline calculus.
Image representation affects both the forward model and quantitative feature recovery in model-based iterative CT reconstruction.
Extractive summary: sentences quoted from the sources.
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