mlsanityData to 7 Oct 2026

Diffusion-based Generative Image Outpainting for Recovery of FOV-Truncated CT Images

arXiv 2406.04769 ↗First posted 7 Jun 2024eess.IV cs.CV cs.LGcited by 4

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Field-of-view (FOV) recovery of truncated chest CT scans is crucial for accurate body composition analysis, which involves quantifying skeletal muscle and subcutaneous adipose tissue (SAT) on CT slices. This, in turn, enables disease prognostication. Here, we present a method for recovering truncated CT slices using generative image outpainting. We train a diffusion model and apply it to truncated CT slices generated by simulating a small FOV. Our model reliably recovers the truncated anatomy and outperforms the previous state-of-the-art despite being trained on 87% less data.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 11 of its 22 references that are in the map. First posted on arXiv 7 Jun 2024.

Builds on

11 of 22 references.

References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  7. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  8. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 620
  9. 2020Recurrent Feature Reasoning for Image Inpaintingcited by 107
  10. 2019Field of View Extension in Computed Tomography Using Deep Learning Priorcited by 6
  11. 2019CT Field of View Extension Using Combined Channels Extension and Deep Learning Methodscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Efficient Image-to-Image Schr"odinger Bridge for CT Field of View Extensioncited by 1
  2. 2026Texture-preserving implicit neural representation for Cone beam CT truncated reconstructioncited by 0
  3. 2026Wavelet-Optimized Pseudo-3D Accelerated Diffusion Model for Truncated Computed Laminographycited by 0
  4. 2026Field-of-View Extension in Dental Cone-Beam CT via Implicit Neural Representations and Diffusion Model-Based Refinementcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior
  2. 2024Exploring Out-of-distribution Detection for Sparse-view Computed Tomography with Diffusion Models
  3. 2026A Diffusion-Based Generative Prior Approach to Sparse-view Computed Tomography
  4. 2025When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT
  5. 2026Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions
  6. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
  7. 2024Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models
  8. 2026Towards reconstructing experimental sparse-view X-ray CT data with diffusion models