mlsanityData to 7 Oct 2026

A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction

arXiv 2105.11692 ↗First posted 25 May 2021cs.CV cs.LG eess.IVcited by 11

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Abstract (arXiv)

Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design and implementation have potential flaws. Fundamentally, most deep learning models are driven entirely by data without consideration of any prior knowledge, which dramatically increases the complexity of neural networks and limits the application scope and model generalizability. Here we establish a geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction. We introduce a novel mechanism for integrating geometric priors of the imaging system. We demonstrate that the seamless inclusion of known priors is essential to enhance the performance of 3D volumetric computed tomography imaging with ultra-sparse sampling. The study opens new avenues for data-driven biomedical imaging and promises to provide substantially improved imaging tools for various clinical imaging and image-guided interventions.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 36 of its 81 references that are in the map. First posted on arXiv 25 May 2021.

Builds on

36 of 81 references.

References · 36

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24 more
  1. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 290
  2. 2019What Do Single-view 3D Reconstruction Networks Learn?cited by 259
  3. 2017Learned Primal-dual Reconstructioncited by 251
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 4; 2025: 2; 2026: 1.

11 papers.

  1. 2024DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rayscited by 8
  2. 2022X-ray Dissectography Improves Lung Nodule Detectioncited by 1
  3. 2023SdCT-GAN: Reconstructing CT from Biplanar X-Rays with Self-driven Generative Adversarial Networkscited by 1
  4. 2024Low-resolution Prior Equilibrium Network for CT Reconstructioncited by 1
  5. 2025Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surfacecited by 1
  6. 2021X-ray Dissectography Enables Stereotography to Improve Diagnostic Performancecited by 0
  7. 2022Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinuscited by 0
  8. 2024GuidedRec: Guiding Ill-Posed Unsupervised Volumetric Recoverycited by 0
  9. 2024Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imagingcited by 0
  10. 2025Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep Learningcited by 0
  11. 2026Deep learning Based Correction Algorithms for 3D Medical Reconstruction in Computed Tomography and Macroscopic Imagingcited by 0