Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction

arXiv:2604.21960 · eess.IV, cs.CV, cs.LG · Submitted 2026-04-23 · Read on arXiv

eess.IV, cs.CV, cs.LG

Submitted: 2026-04-23

Updated: 2026-09-01

Code: https://github.com/SwissDataScienceCenter/cbct_cdpa

Terminology

• Consistency — 58× in this paper · explained in 3 episodes
• Diffusion Model — 31× in this paper · explained in 8 episodes
• Alignment — 24× in this paper · explained in 1 episode
• Fine-Tuning — 21× in this paper · explained in 5 episodes
• Diffusion Models — 17× in this paper · explained in 6 episodes
• Ground Truth — 15× in this paper
• Sparsity — 13× in this paper · explained in 4 episodes
• Computed Tomography — 11× in this paper
• AUC — 9× in this paper · explained in 2 episodes
• U-net architecture — 9× in this paper · explained in 3 episodes
• Inverse Problem — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Conditional Diffusion Posterior Alignment — 7× in this paper
• Fidelity — 7× in this paper · explained in 2 episodes
• Reconstruction Error — 5× in this paper · explained in 2 episodes
• Uncertainty Quantification — 5× in this paper · explained in 8 episodes
• Diffusion Posterior Sampling — 4× in this paper
• Ablation study — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Deep Image Prior — 3× in this paper
• Hallucination — 3× in this paper · explained in 4 episodes
• Loss Function — 3× in this paper · explained in 2 episodes
• Score Function — 3× in this paper · explained in 4 episodes
• Swiss Data Science Center — 3× in this paper
• Benchmark — 2× in this paper · explained in 2 episodes
• Denoising Diffusion Implicit Models — 2× in this paper
• Latent Space — 2× in this paper · explained in 7 episodes
• Paul Scherrer Institute — 2× in this paper
• Peak Signal-to-Noise Ratio — 2× in this paper
• Receiver Operating Characteristic — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Mean Squared Error — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography
• CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
• SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fields
• Principled Confidence Estimation for Deep Computed Tomography
• Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
• Denoising Diffusion Implicit Models

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