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Found 5 papers, 2 papers with code
SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap
For medical CT images, the through-plane resolution is often worse than the in-plane resolution and there can be overlap between slices, causing difficulties in diagnoses.
Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction
Recognizing that CT often involves scanning similar subjects, we propose a novel approach to improve reconstruction quality through joint reconstruction of multiple objects using INRs.
Multi-stage Deep Learning Artifact Reduction for Computed Tomography
As an alternative, we propose a multi-stage deep learning method for artifact removal, in which neural networks are applied to several domains, similar to a classical CT processing pipeline.
A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks
Moreover, we show that the training time of an NN-FDK network is orders of magnitude lower than the considered deep neural networks, with only a slight reduction in reconstruction accuracy.
Noise2Inverse: Self-supervised deep convolutional denoising for tomography
Recovering a high-quality image from noisy indirect measurements is an important problem with many applications.
