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Found 3 papers, 1 papers with code
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.
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