mlsanityData to 14 Oct 2026

Differentiable Forward Projector for X-ray Computed Tomography

arXiv 2307.05801 ↗First posted 11 Jul 2023cs.LG cs.CV cs.MScited by 14

Not on the map. Why?

Abstract (arXiv)

Data-driven deep learning has been successfully applied to various computed tomographic reconstruction problems. The deep inference models may outperform existing analytical and iterative algorithms, especially in ill-posed CT reconstruction. However, those methods often predict images that do not agree with the measured projection data. This paper presents an accurate differentiable forward and back projection software library to ensure the consistency between the predicted images and the original measurements. The software library efficiently supports various projection geometry types while minimizing the GPU memory footprint requirement, which facilitates seamless integration with existing deep learning training and inference pipelines. The proposed software is available as open source: https://github.com/LLNL/LEAP.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 11 Jul 2023.

Builds on

10 of 27 references.

References · 10

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2017Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CTcited by 88
  3. 2017CNN-Based Projected Gradient Descent for Consistent Image Reconstructioncited by 77
  4. 2022DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstructioncited by 74
  5. 2017Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completioncited by 31
  6. 2022Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Datacited by 17
  7. 2019Diffraction tomography with a deep image priorcited by 15
  8. 2019Extreme Few-view CT Reconstruction using Deep Inferencecited by 5
  9. 2022LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstructioncited by 3
  10. 2019Improving Limited Angle CT Reconstruction with a Robust GAN Priorcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 5; 2026: 5.

14 papers.

Sort:

By area:

  1. 2023CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Modelcited by 6
  2. 2026DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstructioncited by 3
  3. 2024Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstructioncited by 2
  4. 2024Neural rendering enables dynamic tomographycited by 1
  5. 2025Virtual-mask Informed Prior for Sparse-view Dual-Energy CT Reconstructioncited by 1
  6. 2025Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Modelscited by 1
  7. 2025Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CTcited by 1
  8. 2024Sequential-Scanning Dual-Energy CT Imaging Using High Temporal Resolution Image Reconstruction and Error-Compensated Material Basis Image Generationcited by 0
  9. 2025Pseudoinverse Diffusion Models for Generative CT Image Reconstruction from Low Dose Datacited by 0
  10. 2025Tada-DIP: Input-adaptive Deep Image Prior for One-shot 3D Image Reconstructioncited by 0
  11. 2026Efficient Noise Context-Aware Representation for Low-Dose CT Denoisingcited by 0
  12. 2026Splat-based Metal Artifact Reduction in Cone-Beam CT via Polychromatic Modelingcited by 0
2 more
  1. 2026Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Modelcited by 0
  2. 2026Plug-and-Play Methods Provably Converge Even with Improperly Trained Denoisers: Convergence by Architectural Designcited by 0