mlsanityData to 7 Oct 2026

DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rays

arXiv 2407.13545 ↗First posted 18 Jul 2024eess.IV cs.CVcited by 9

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Abstract (arXiv)

Computed tomography (CT) is widely utilized in clinical settings because it delivers detailed 3D images of the human body. However, performing CT scans is not always feasible due to radiation exposure and limitations in certain surgical environments. As an alternative, reconstructing CT images from ultra-sparse X-rays offers a valuable solution and has gained significant interest in scientific research and medical applications. However, it presents great challenges as it is inherently an ill-posed problem, often compromised by artifacts resulting from overlapping structures in X-ray images. In this paper, we propose DiffuX2CT, which models CT reconstruction from orthogonal biplanar X-rays as a conditional diffusion process. DiffuX2CT is established with a 3D global coherence denoising model with a new, implicit conditioning mechanism. We realize the conditioning mechanism by a newly designed tri-plane decoupling generator and an implicit neural decoder. By doing so, DiffuX2CT achieves structure-controllable reconstruction, which enables 3D structural information to be recovered from 2D X-rays, therefore producing faithful textures in CT images. As an extra contribution, we collect a real-world lumbar CT dataset, called LumbarV, as a new benchmark to verify the clinical significance and performance of CT reconstruction from X-rays. Extensive experiments on this dataset and three more publicly available datasets demonstrate the effectiveness of our proposal.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 65 of its 92 references that are in the map. First posted on arXiv 18 Jul 2024.

Builds on

65 of 92 references.

References · 65

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38 more
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50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 6.

9 papers.

  1. 2025DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rayscited by 3
  2. 2025Bidirectional Mammogram View Translation with Column-Aware and Implicit 3D Conditional Diffusioncited by 1
  3. 2025ReBrain: Brain MRI Reconstruction from Sparse CT Slice via Retrieval-Augmented Diffusioncited by 0
  4. 2026DuFal: Dual-Frequency-Aware Learning for High-Fidelity Extremely Sparse-view CBCT Reconstructioncited by 0
  5. 2026Exploiting Completeness Perception with Diffusion Transformer for Unified 3D MRI Synthesiscited by 0
  6. 2026Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rayscited by 0
  7. 2026PSCT-Net: Geometry-Aware Pediatric Skull CT Reconstruction via Differentiable Back-Projection and Attention-Guided Refinementcited by 0
  8. 2026From Sparse X-rays to 3D CT: Training-Free Reconstruction with Diffusion Priorscited by 0
  9. 2026Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstructioncited by 0