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Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT

arXiv 1708.08333 ↗First posted 28 Aug 2017cs.CV cs.LG stat.MLcited by 87

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Abstract (arXiv)

X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse- view CT reconstruction. However, theoretical justification is still lacking. Inspired by the recent theory of deep convolutional framelets, the main goal of this paper is, therefore, to reveal the limitation of U-Net and propose new multi-resolution deep learning schemes. In particular, we show that the alternative U- Net variants such as dual frame and the tight frame U-Nets satisfy the so-called frame condition which make them better for effective recovery of high frequency edges in sparse view- CT. Using extensive experiments with real patient data set, we demonstrate that the new network architectures provide better reconstruction performance.

Key facts. Cited by 87 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Aug 2017.

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Citing papers per year: 2021: 16; 2022: 17; 2023: 18; 2024: 16; 2025: 13; 2026: 7.

87 papers.

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  1. 2022Transformers in Medical Imaging: A Surveycited by 245
  2. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistencycited by 180
  3. 2022DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstructioncited by 73
  4. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstructioncited by 64
  5. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  6. 2021DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoisingcited by 46
  7. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  8. 2022Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomographycited by 31
  9. 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
  10. 2022Generative Modeling in Sinogram Domain for Sparse-view CT Reconstructioncited by 27
  11. 2022Online Deep Equilibrium Learning for Regularization by Denoisingcited by 22
  12. 2021Modality specific U-Net variants for biomedical image segmentation: A surveycited by 20
38 more
  1. 2021Data augmentation for deep learning based accelerated MRI reconstruction with limited datacited by 19
  2. 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
  3. 2024DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstructioncited by 17
  4. 2022Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstructioncited by 16
  5. 2023Differentiable Forward Projector for X-ray Computed Tomographycited by 14
  6. 2024DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstructioncited by 13
  7. 2021A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstructioncited by 11
  8. 2022Robust Split Federated Learning for U-shaped Medical Image Networkscited by 11
  9. 2024Implicit Neural Representations for Robust Joint Sparse-View CT Reconstructioncited by 9
  10. 2022Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRIcited by 8
  11. 2023Block Coordinate Plug-and-Play Methods for Blind Inverse Problemscited by 8
  12. 2022WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layercited by 7
  13. 2024TOGS: Gaussian Splatting with Temporal Opacity Offset for Real-Time 4D DSA Renderingcited by 7
  14. 2024Physics-Driven Autoregressive State Space Models for Medical Image Reconstructioncited by 7
  15. 2021Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstructioncited by 6
  16. 2021Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstructioncited by 6
  17. 2024GaSpCT: Gaussian Splatting for Novel CT Projection View Synthesiscited by 6
  18. 2024Discretized Gaussian Representation for Tomographic Reconstructioncited by 6
  19. 2022Spectral2Spectral: Image-spectral Similarity Assisted Spectral CT Deep Reconstruction without Referencecited by 5
  20. 2023TiAVox: Time-aware Attenuation Voxels for Sparse-view 4D DSA Reconstructioncited by 5
  21. 2021ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learningcited by 4
  22. 2023Robust Physics-based Deep MRI Reconstruction Via Diffusion Purificationcited by 4
  23. 2022LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstructioncited by 3
  24. 2022Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20× Speedupcited by 3
  25. 2023SMUG: Towards robust MRI reconstruction by smoothed unrollingcited by 3
  26. 2023Data-iterative Optimization Score Model for Stable Ultra-Sparse-View CT Reconstructioncited by 3
  27. 2023Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Snow Layers in Radar Echogramscited by 3
  28. 2024RN-SDEs: Limited-Angle CT Reconstruction with Residual Null-Space Diffusion Stochastic Differential Equationscited by 3
  29. 2021A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstructioncited by 2
  30. 2021Sparse-View CT Reconstruction using Recurrent Stacked Back Projectioncited by 2
  31. 2023Improving Automated Hemorrhage Detection in Sparse-view Computed Tomography via Deep Convolutional Neural Network based Artifact Reductioncited by 2
  32. 2025Prompting Lipschitz-constrained network for multiple-in-one sparse-view CT reconstructioncited by 2
  33. 2021UVTomo-GAN: An adversarial learning based approach for unknown view X-ray tomographic reconstructioncited by 1
  34. 2023Computational Modeling of Deep Multiresolution-Fractal Texture and Its Application to Abnormal Brain Tissue Segmentationcited by 1
  35. 2023Improving image quality of sparse-view lung tumor CT images with U-Netcited by 1
  36. 2023OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarioscited by 1
  37. 2023Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluatorcited by 1
  38. 2024Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstructioncited by 1

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