DICE integrates two-agent consensus equilibrium into diffusion model sampling to enforce both measurement consistency and generative image priors for improved sparse-view CT reconstruction.
Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
An unsupervised perceptual attention network framework effectively denoises real low-dose liver CT images and meets clinical validation by physicians.
citing papers explorer
-
DICE: Diffusion Consensus Equilibrium for Sparse-view CT Reconstruction
DICE integrates two-agent consensus equilibrium into diffusion model sampling to enforce both measurement consistency and generative image priors for improved sparse-view CT reconstruction.
-
Unsupervised Denoising of Real Clinical Low Dose Liver CT with Perceptual Attention Networks
An unsupervised perceptual attention network framework effectively denoises real low-dose liver CT images and meets clinical validation by physicians.