144 citations · 155 across the 13 of their papers we have counts for
15 papers
Does Compression Imply Generalization? A Minimum Description Length Perspective
Lincen Yang, Jiayang Shi, Nan Pu +4
A central question in modern learning theory is what notion of compression, if any, explains why over-parameterized neural networks generalize. Existing answers measure complexity…
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…
Deep Reinforcement Learning for Optimizing Angle Selection and Dose Allocation in CT Reconstruction
Tianyuan Wang, Daniël M. Pelt, Felix Lucka +2
Traditional X-ray computed tomography (CT) scanning strategies typically select projection angles uniformly and allocate dose equally. In practice, however, CT scans often need to…
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…
Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces
Alejandro García-Castellanos, David R. Wessels, Nicky J. van den Berg +3
We introduce Equivariant Neural Eikonal Solvers, a novel framework that integrates Equivariant Neural Fields (ENFs) with Neural Eikonal Solvers. Our approach employs a single neura…
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…
A tomographic workflow to enable deep learning for X-ray based foreign object detection
Mathé T. Zeegers, Tristan van Leeuwen, Daniël M. Pelt +3
Detection of unwanted (`foreign') objects within products is a common procedure in many branches of industry for maintaining production quality. X-ray imaging is a fast, non-invasi…
Improving reproducibility in synchrotron tomography using implementation-adapted filters
Poulami Somanya Ganguly, Daniël M. Pelt, Doga Gürsoy +2
For reconstructing large tomographic datasets fast, filtered backprojection-type or Fourier-based algorithms are still the method of choice, as they have been for decades. These ro…
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains
Richard Schoonhoven, Allard A. Hendriksen, Daniël M. Pelt +1
Neural network pruning techniques can substantially reduce the computational cost of applying convolutional neural networks (CNNs). Common pruning methods determine which convoluti…
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…
A method for locally approximating regularized iterative tomographic reconstruction methods
D. M. Pelt, K. J. Batenburg
In many applications of tomography, the acquired projections are either limited in number or contain a significant amount of noise. In these cases, standard reconstruction methods…