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TorchRadon: Fast Differentiable Routines for Computed Tomography

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arxiv 2009.14788 v1 pith:DJIBQQ6U submitted 2020-09-29 eess.IV cs.CVcs.LG

Matteo Ronchetti This is my paper · ORCID
classification eess.IVcs.CVcs.LG
keywords torchradonexistinglibrarycomputeddeepdifferentiablefastlearning
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction

    eess.IV 2025-06 conditional novelty 7.0 of 10

    A dual-domain diffusion model trained only on normal-dose CT data generalizes to unseen low-dose levels by matching Poisson noise in projections and refining images with double guidance.

  2. Continuous 3-D Latent Diffusion for Medical Generation and Reconstruction

    physics.med-ph 2026-07 conditional novelty 6.0 of 10

    A coordinate-conditioned implicit decoder makes 3D latent diffusion practical on a single GPU, autoencoding 512^3 CT in about 10 s while supporting both generation and measurement-guided reconstruction from one frozen prior.

  3. Cross-view Generalized Diffusion Model for Sparse-view CT Reconstruction

    eess.IV 2025-08 conditional novelty 6.0 of 10

    CvG-Diff reformulates sparse-view CT reconstruction as a deterministic generalized diffusion process, achieving 38.34 dB PSNR for 18-view CT in 10 steps on AAPM-LDCT.

  4. Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction

    eess.IV 2025-02 conditional novelty 5.0 of 10

    Spener is an unsupervised iterative network that uses a self-generated prior image to stabilize sparse-view CT reconstruction, matching or beating supervised baselines.

  5. Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data

    cs.CV 2025-01 conditional novelty 5.0 of 10

    ADDGAN learns clean image distributions from noisy CT and DBT measurements by feeding generated objects through the known imaging operator, and it beats AmbientGAN baselines on FID and observer-task metrics.

  6. Solving Blind Inverse Problems: Adaptive Diffusion Models for Motion-corrected Sparse-view 4DCT

    physics.med-ph 2025-01 conditional novelty 5.0 of 10

    A diffusion-based method that jointly reconstructs motion-corrected sparse-view 4DCT images and estimates respiratory motion, outperforming three baselines on XCAT phantoms.

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