A review of deep learning ct reconstruction from incomplete projection data
Computed tomography (CT) is a widely used imaging technique in both medical and
industrial applications. However, accurate CT reconstruction requires complete projection …
industrial applications. However, accurate CT reconstruction requires complete projection …
Multi-channel optimization generative model for stable ultra-sparse-view CT reconstruction
Score-based generative model (SGM) has risen to prominence in sparse-view CT
reconstruction due to its impressive generation capability. The consistency of data is crucial …
reconstruction due to its impressive generation capability. The consistency of data is crucial …
Dual-domain collaborative diffusion sampling for multi-source stationary computed tomography reconstruction
The multi-source stationary CT, where both the detector and X-ray source are fixed,
represents a novel imaging system with high temporal resolution that has garnered …
represents a novel imaging system with high temporal resolution that has garnered …
A dual-domain diffusion model for sparse-view ct reconstruction
To reduce the radiation dose, sparse-view computed tomography (CT) reconstruction has
been proposed, aiming to recover high-quality CT images from sparsely sampled sinogram …
been proposed, aiming to recover high-quality CT images from sparsely sampled sinogram …
Stage-by-stage wavelet optimization refinement diffusion model for sparse-view CT reconstruction
K Xu, S Lu, B Huang, W Wu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Diffusion model has emerged as a potential tool to tackle the challenge of sparse-view CT
reconstruction, displaying superior performance compared to conventional methods …
reconstruction, displaying superior performance compared to conventional methods …
Data-iterative optimization score model for stable ultra-sparse-view CT reconstruction
W Wu, Y Wang - arXiv preprint arXiv:2308.14437, 2023 - arxiv.org
Score-based generative models (SGMs) have gained prominence in sparse-view CT
reconstruction for their precise sampling of complex distributions. In SGM-based …
reconstruction for their precise sampling of complex distributions. In SGM-based …
Physics-Inspired Generative Models in Medical Imaging: A Review
D Hein, A Bozorgpour, D Merhof, G Wang - arXiv preprint arXiv …, 2024 - arxiv.org
Physics-inspired Generative Models (GMs), in particular Diffusion Models (DMs) and
Poisson Flow Models (PFMs), enhance Bayesian methods and promise great utility in …
Poisson Flow Models (PFMs), enhance Bayesian methods and promise great utility in …
CT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model
Purpose Recently, diffusion posterior sampling (DPS), where score-based diffusion priors
are combined with likelihood models, has been used to produce high-quality computed …
are combined with likelihood models, has been used to produce high-quality computed …
Lightweight diffusion models: a survey
W Song, W Ma, M Zhang, Y Zhang, X Zhao - Artificial Intelligence Review, 2024 - Springer
Diffusion models (DMs) are a type of potential generative models, which have achieved
better effects in many fields than traditional methods. DMs consist of two main processes …
better effects in many fields than traditional methods. DMs consist of two main processes …
Stationary CT Imaging of Intracranial Hemorrhage with Diffusion Posterior Sampling Reconstruction
A Lopez-Montes, T McSkimming, A Skeats… - arXiv preprint arXiv …, 2024 - arxiv.org
Diffusion Posterior Sampling (DPS) can be used in Computed Tomography (CT)
reconstruction by leveraging diffusion-based generative models for unconditional image …
reconstruction by leveraging diffusion-based generative models for unconditional image …