Diffusion priors for sparse-view CT work on synthetic data but face domain shift and forward model mismatch on experimental phantom data, with annealed likelihood weights offering partial mitigation.
Solving inverse problems in medical imaging with score-based generative models,
2 Pith papers cite this work. Polarity classification is still indexing.
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Equivariance2Inverse merges equivariant imaging and sparse reconstruction into a self-supervised CT method that remains effective under scintillator blurring and limited-angle geometries, outperforming prior methods on real 2DeteCT data.
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Towards reconstructing experimental sparse-view X-ray CT data with diffusion models
Diffusion priors for sparse-view CT work on synthetic data but face domain shift and forward model mismatch on experimental phantom data, with annealed likelihood weights offering partial mitigation.
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Equivariance2Inverse: A Practical Self-Supervised CT Reconstruction Method Benchmarked on Real, Limited-Angle, and Blurred Data
Equivariance2Inverse merges equivariant imaging and sparse reconstruction into a self-supervised CT method that remains effective under scintillator blurring and limited-angle geometries, outperforming prior methods on real 2DeteCT data.