4 citations · 4 across the 5 of their papers we have counts for
5 papers
Efficient Flow Matching for Sparse-View CT Reconstruction
Jiayang Shi, Lincen Yang, Zhong Li +3
Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill-posed in…
DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
Jiayang Shi, Daniel M. Pelt, K. Joost Batenburg
Diffusion models have recently emerged as powerful priors for solving inverse problems. While computed tomography (CT) is theoretically a linear inverse problem, it poses many prac…
SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap
Jiayang Shi, Daniel M. Pelt, K. Joost Batenburg
Computed tomography (CT) is a widely used non-invasive medical imaging technique for disease diagnosis. The diagnostic accuracy is often affected by image resolution, which can be…
Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction
Jiayang Shi, Junyi Zhu, Daniel M. Pelt +2
Computed Tomography (CT) is pivotal in industrial quality control and medical diagnostics. Sparse-view CT, offering reduced ionizing radiation, faces challenges due to its under-sa…
Multi-stage Deep Learning Artifact Reduction for Pallel-beam Computed Tomography
Jiayang Shi, Daniel M. Pelt, K. Joost Batenburg
Computed Tomography (CT) using synchrotron radiation is a powerful technique that, compared to lab-CT techniques, boosts high spatial and temporal resolution while also providing a…