CDPIR integrates cross-distribution diffusion priors from a Scalable Interpolant Transformer trained with classifier-free guidance into model-based iterative reconstruction to improve sparse-view CT under out-of-distribution conditions.
Deep con- volutional neural network for inverse problems in imaging,
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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.
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.
citing papers explorer
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Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT
CDPIR integrates cross-distribution diffusion priors from a Scalable Interpolant Transformer trained with classifier-free guidance into model-based iterative reconstruction to improve sparse-view CT under out-of-distribution conditions.
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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.