mlsanityData to 14 Oct 2026

Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey

arXiv 2203.15725 ↗First posted 29 Mar 2022eess.IV cs.LG eess.SPcited by 14

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Abstract (arXiv)

Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks often suffer from the black box nature and major issues such as instabilities, which is a major barrier to apply deep learning methods in low-dose CT applications. An emerging trend is to integrate imaging physics and model into deep networks, enabling a hybridization of physics/model-based and data-driven elements. %This type of hybrid methods has become increasingly influential. In this paper, we systematically review the physics/model-based data-driven methods for LDCT, summarize the loss functions and training strategies, evaluate the performance of different methods, and discuss relevant issues and future directions.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 22 of its 51 references that are in the map. First posted on arXiv 29 Mar 2022.

Builds on

22 of 51 references.

References · 22

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10 more
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Citing papers per year: 2023: 4; 2024: 2; 2025: 6; 2026: 2.

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  1. 2023Diffusion Prior Regularized Iterative Reconstruction for Low-dose CTcited by 9
  2. 2023Parallel Diffusion Model-based Sparse-view Cone-beam Breast CTcited by 7
  3. 2024PINNs for Medical Image Analysis: A Surveycited by 5
  4. 2023CMISR: Circular Medical Image Super-Resolutioncited by 2
  5. 2025Federated Learning for Large Models in Medical Imaging: A Comprehensive Reviewcited by 2
  6. 2025Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Modelcited by 1
  7. 2023Privacy-Preserving Encrypted Low-Dose CT Denoisingcited by 0
  8. 2024Integrating Deep Unfolding with Direct Diffusion Bridges for Computed Tomography Reconstructioncited by 0
  9. 2025Physical foundations for trustworthy medical imaging: a review for artificial intelligence researcherscited by 0
  10. 2025An incremental algorithm for non-convex AI-enhanced medical image processingcited by 0
  11. 2025Limited-Angle Tomography Reconstruction via Projector Guided 3D Diffusioncited by 0
  12. 2025Improving the Generalisation of Learned Reconstruction Frameworkscited by 0
2 more
  1. 2026SynthRAR: Ring Artifacts Reduction in CT with Unrolled Network and Synthetic Data Trainingcited by 0
  2. 2026Deep Unrolled Networks in Representation Space Applied to MRI Reconstructioncited by 0