51 citations · 51 across the 4 of their papers we have counts for
7 papers · 1 filter
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…
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…
Benchmarking learned algorithms for computed tomography image reconstruction tasks
Maximilian B. Kiss, Ander Biguri, Zakhar Shumaylov +4
Computed tomography (CT) is a widely used non-invasive diagnostic method in various fields, and recent advances in deep learning have led to significant progress in CT image recons…
Quantifying the effect of X-ray scattering for data generation in real-time defect detection
Vladyslav Andriiashen, Robert van Liere, Tristan van Leeuwen +1
Background: X-ray imaging is widely used for the non-destructive detection of defects in industrial products on a conveyor belt. In-line detection requires highly accurate, robust,…
Learned denoising with simulated and experimental low-dose CT data
Maximilian B. Kiss, Ander Biguri, Carola-Bibiane Schönlieb +2
Like in many other research fields, recent developments in computational imaging have focused on developing machine learning (ML) approaches to tackle its main challenges. To impro…