mlsanityData to 7 Oct 2026

TorchRadon: Fast Differentiable Routines for Computed Tomography

arXiv 2009.14788 ↗First posted 29 Sep 2020eess.IV cs.CV cs.LGcited by 44

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Abstract (arXiv)

This work presents TorchRadon -- an open source CUDA library which contains a set of differentiable routines for solving computed tomography (CT) reconstruction problems. The library is designed to help researchers working on CT problems to combine deep learning and model-based approaches. The package is developed as a PyTorch extension and can be seamlessly integrated into existing deep learning training code. Compared to the existing Astra Toolbox, TorchRadon is up to 125 faster. The operators implemented by TorchRadon allow the computation of gradients using PyTorch backward(), and can therefore be easily inserted inside existing neural networks architectures. Because of its speed and GPU support, TorchRadon can also be effectively used as a fast backend for the implementation of iterative algorithms. This paper presents the main functionalities of the library, compares results with existing libraries and provides examples of usage.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2020.

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Citing papers per year: 2021: 4; 2022: 8; 2023: 9; 2024: 8; 2025: 7; 2026: 8.

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  1. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 514
  2. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistencycited by 180
  3. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problemscited by 129
  4. 2023Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoisingcited by 21
  5. 2023FreeSeed: Frequency-band-aware and Self-guided Network for Sparse-view CT Reconstructioncited by 21
  6. 2024MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimationcited by 19
  7. 2021Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Futurecited by 18
  8. 2023Learning to Distill Global Representation for Sparse-View CTcited by 14
  9. 2025Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstructioncited by 11
  10. 2022Uncertainty Quantification for Deep Unrolling-Based Computational Imagingcited by 10
  11. 2022On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Settingcited by 9
  12. 2022Quad-Net: Quad-domain Network for CT Metal Artifact Reductioncited by 8
32 more
  1. 2022Gradient-Based Geometry Learning for Fan-Beam CT Reconstructioncited by 8
  2. 2021Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstructioncited by 6
  3. 2022Deep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Densitycited by 6
  4. 2024Glimpse: Generalized Locality for Scalable and Robust CTcited by 6
  5. 2022Data Models for Dataset Drift Controls in Machine Learning With Optical Imagescited by 5
  6. 2023Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstructioncited by 5
  7. 2025Cross-view Generalized Diffusion Model for Sparse-view CT Reconstructioncited by 5
  8. 2023NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problemscited by 4
  9. 2021Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domaincited by 3
  10. 2022Training Adaptive Reconstruction Networks for Blind Inverse Problemscited by 3
  11. 2025Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstructioncited by 3
  12. 2023Deep Radon Prior: A Fully Unsupervised Framework for Sparse-View CT Reconstructioncited by 2
  13. 2024Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CTcited by 2
  14. 2025Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Datacited by 2
  15. 2023MCDIP-ADMM: Overcoming Overfitting in DIP-based CT reconstructioncited by 1
  16. 2024Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstructioncited by 1
  17. 2024MVMS-RCN: A Dual-Domain Unfolding CT Reconstruction with Multi-sparse-view and Multi-scale Refinement-correctioncited by 1
  18. 2024CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Ratescited by 1
  19. 2025Data-Efficient Limited-Angle CT Using Deep Priors and Regularizationcited by 1
  20. 2026Low performing pixel correction in computed tomography with unrolled network and synthetic data trainingcited by 1
  21. 2022Sinogram Enhancement with Generative Adversarial Networks using Shape Priorscited by 0
  22. 2024CoCPF: Coordinate-based Continuous Projection Field for Ill-Posed Inverse Problem in Imagingcited by 0
  23. 2024Frequency-regularized Neural Representation Method for Sparse-view Tomographic Reconstructioncited by 0
  24. 2025Interlaced dynamic XCT reconstruction with spatio-temporal implicit neural representationscited by 0
  25. 2025An update to PYRO-NN: A Python Library for Differentiable CT Operatorscited by 0
  26. 2026SynthRAR: Ring Artifacts Reduction in CT with Unrolled Network and Synthetic Data Trainingcited by 0
  27. 2026SCOUT: Fast Spectral CT Imaging in Ultra LOw-data Regimes via PseUdo-label GeneraTioncited by 0
  28. 2026ReCo-Diff: Residual-Conditioned Deterministic Sampling for Cold Diffusion in Sparse-View CTcited by 0
  29. 2026MedQ-Deg: A Multidimensional Benchmark for Evaluating MLLMs Across Medical Image Quality Degradationscited by 0
  30. 2026Variational Garrote for Sparse Inverse Problemscited by 0
  31. 2026Neighbor2Inverse: Self-Supervised Denoising for Low-Dose Region-of-Interest Phase Contrast CTcited by 0
  32. 2026FrequencyCT: Frequency Domain Self-supervised Low-dose CT Denoisingcited by 0