4 citations · 4 across the 6 of their papers we have counts for
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Fast reconstruction of tensor tomographic X-ray scattering data for real-time applications
André M. Antunes, Daniël M. Pelt, K. Joost Batenburg
X-ray scattering tensor tomography reveals nanoscale structural orientation in 3D, but its reliance on slow iterative reconstruction limits real-time use. We introduce an extension…
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…
A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks
Marinus J. Lagerwerf, Daniel M Pelt, Willem Jan Palenstijn +1
Circular cone-beam (CCB) Computed Tomography (CT) has become an integral part of industrial quality control, materials science and medical imaging. The need to acquire and process…
Noise2Inverse: Self-supervised deep convolutional denoising for tomography
Allard A. Hendriksen, Daniel M. Pelt, K. Joost Batenburg
Recovering a high-quality image from noisy indirect measurements is an important problem with many applications. For such inverse problems, supervised deep convolutional neural net…