mlsanityData to 7 Oct 2026

Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

arXiv 1702.00288 ↗First posted 1 Feb 2017physics.med-ph cs.NEcited by 224

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Cited by 36 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Given the potential X-ray radiation risk to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. The current main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction, but they need to access original raw data whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, the deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases. Especially, our method has been favorably evaluated in terms of noise suppression, structural preservation and lesion detection.

Key facts. Cited by 224 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Feb 2017.

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Citing papers per year: 2021: 41; 2022: 35; 2023: 39; 2024: 35; 2025: 44; 2026: 30.

224 papers.

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  1. 2022Conversion Between CT and MRI Images Using Diffusion and Score-Matching Modelscited by 69
  2. 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalizationcited by 51
  3. 2022CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoisingcited by 46
  4. 2024Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKVcited by 36
  5. 2024Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generationcited by 33
  6. 2022Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomographycited by 31
  7. 2021Eformer: Edge Enhancement based Transformer for Medical Image Denoisingcited by 30
  8. 2024All-In-One Medical Image Restoration via Task-Adaptive Routingcited by 30
  9. 2022Generative Modeling in Sinogram Domain for Sparse-view CT Reconstructioncited by 27
  10. 2022Online Deep Equilibrium Learning for Regularization by Denoisingcited by 22
  11. 2021TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoisingcited by 21
  12. 2023Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoisingcited by 21
38 more
  1. 2022UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomographycited by 19
  2. 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
  3. 2023IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Modelscited by 18
  4. 2022Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Datacited by 17
  5. 2021Uncertainty-Guided Progressive GANs for Medical Image Translationcited by 16
  6. 2022Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstructioncited by 16
  7. 2021Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoisingcited by 15
  8. 2021Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstructioncited by 15
  9. 2023ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoisingcited by 15
  10. 2022Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A surveycited by 14
  11. 2022Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imagingcited by 14
  12. 2023Learning to Distill Global Representation for Sparse-View CTcited by 14
  13. 2021Chest X-Ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findingscited by 13
  14. 2022Low-Dose CT Denoising via Sinogram Inner-Structure Transformercited by 13
  15. 20223D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesiscited by 13
  16. 2022Near-Exact Recovery for Tomographic Inverse Problems via Deep Learningcited by 13
  17. 2021Fourier Image Transformercited by 12
  18. 2021DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstructioncited by 12
  19. 2023LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurringcited by 12
  20. 2022Robust Split Federated Learning for U-shaped Medical Image Networkscited by 11
  21. 2024DenoMamba: A fused state-space model for low-dose CT denoisingcited by 11
  22. 2025Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstructioncited by 11
  23. 2023Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysiscited by 10
  24. 2022Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomographycited by 9
  25. 2022Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priorscited by 9
  26. 2022Computed Tomography Reconstruction using Generative Energy-Based Priorscited by 9
  27. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  28. 2024Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differencescited by 9
  29. 2021Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolationcited by 8
  30. 2023Block Coordinate Plug-and-Play Methods for Blind Inverse Problemscited by 8
  31. 2023Diffusion Prior Regularized Iterative Reconstruction for Low-dose CTcited by 8
  32. 2021Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detectioncited by 7
  33. 2021Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learningcited by 7
  34. 2023Parallel Diffusion Model-based Sparse-view Cone-beam Breast CTcited by 7
  35. 2024Low-dose CT Denoising with Language-engaged Dual-space Alignmentcited by 7
  36. 2024MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applicationscited by 7
  37. 2025Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Priorcited by 7
  38. 2025CTLformer: A Hybrid Denoising Model Combining Convolutional Layers and Self-Attention for Enhanced CT Image Reconstructioncited by 7

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