mlsanityData to 7 Oct 2026

Learned Alternating Minimization Algorithm for Dual-domain Sparse-View CT Reconstruction

arXiv 2306.02644 ↗First posted 5 Jun 2023cs.CVcited by 6

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Abstract (arXiv)

We propose a novel Learned Alternating Minimization Algorithm (LAMA) for dual-domain sparse-view CT image reconstruction. LAMA is naturally induced by a variational model for CT reconstruction with learnable nonsmooth nonconvex regularizers, which are parameterized as composite functions of deep networks in both image and sinogram domains. To minimize the objective of the model, we incorporate the smoothing technique and residual learning architecture into the design of LAMA. We show that LAMA substantially reduces network complexity, improves memory efficiency and reconstruction accuracy, and is provably convergent for reliable reconstructions. Extensive numerical experiments demonstrate that LAMA outperforms existing methods by a wide margin on multiple benchmark CT datasets.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 5 Jun 2023.

Builds on

14 of 30 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 431
  3. 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 411
  4. 2017Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)cited by 224
  5. 2018Deep-neural-network based sinogram synthesis for sparse-view CT image reconstructioncited by 47
  6. 2017Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problemscited by 40
  7. 2019ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimizationcited by 37
  8. 2020MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstructioncited by 19
  9. 2022A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesiscited by 16
  10. 2020Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstructioncited by 15
  11. 2020LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CTcited by 14
  12. 2019Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimizationcited by 12
2 more
  1. 2021DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstructioncited by 12
  2. 2021Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstructioncited by 6

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 2.

6 papers.

  1. 20233DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representationcited by 24
  2. 2024ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Imagescited by 19
  3. 2025Prompting Lipschitz-constrained network for multiple-in-one sparse-view CT reconstructioncited by 2
  4. 2024LAMA: Stable Dual-Domain Deep Reconstruction For Sparse-View CTcited by 0
  5. 2024A Learned Proximal Alternating Minimization Algorithm and Its Induced Network for a Class of Two-block Nonconvex and Nonsmooth Optimizationcited by 0
  6. 2025LAMA-Net: A Convergent Network Architecture for Dual-Domain Reconstructioncited by 0