mlsanityData to 14 Oct 2026

Neural Radiance Fields in Medical Imaging: A Survey

arXiv 2402.17797 ↗First posted 26 Feb 2024eess.IV cs.CVcited by 7

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Abstract (arXiv)

Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data. However, they face unique challenges when applied to medical applications. This paper presents a comprehensive examination of applications of NeRFs in medical imaging, highlighting four imminent challenges, including fundamental imaging principles, inner structure requirement, object boundary definition, and color density significance. We discuss current methods on different organs and discuss related limitations. We also review several datasets and evaluation metrics and propose several promising directions for future research.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 23 of its 51 references that are in the map. First posted on arXiv 26 Feb 2024.

Builds on

23 of 51 references.

References · 23

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  2. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  3. 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,584
  4. 2020GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldscited by 785
  5. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 689
  6. 2023On the Challenges and Perspectives of Foundation Models for Medical Image Analysiscited by 135
  7. 2022Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgerycited by 123
  8. 2022NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstructioncited by 108
  9. 2023Implicit Neural Representation in Medical Imaging: A Comparative Surveycited by 90
  10. 2019X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networkscited by 85
  11. 2023Structure-Aware Sparse-View X-ray 3D Reconstructioncited by 72
  12. 2022MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-raycited by 66
11 more
  1. 2022Reinforcement Learning with Neural Radiance Fieldscited by 43
  2. 2023Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstructioncited by 42
  3. 2023CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolutioncited by 28
  4. 2023Ultra-NeRF: Neural Radiance Fields for Ultrasound Imagingcited by 27
  5. 2023BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance Fieldscited by 27
  6. 2022SNAF: Sparse-view CBCT Reconstruction with Neural Attenuation Fieldscited by 23
  7. 2023Efficient Deformable Tissue Reconstruction via Orthogonal Neural Planecited by 20
  8. 2023Perspective Projection-Based 3D CT Reconstruction from Biplanar X-rayscited by 18
  9. 2023Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstructioncited by 12
  10. 2023UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fieldscited by 10
  11. 2024Artificial Intelligence in Image-based Cardiovascular Disease Analysiscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025InnerGS: Internal Scenes Reconstruction and Segmentation via Factorized 3D Gaussian Splattingcited by 3
  2. 2025ViT-NeBLa: A Hybrid Vision Transformer and Neural Beer-Lambert Framework for Single-View 3D Reconstruction of Oral Anatomy from Panoramic Radiographscited by 2
  3. 2025QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representationscited by 1
  4. 2026M-Gaussian: An Magnetic Gaussian Framework for Efficient Multi-Stack MRI Reconstructioncited by 1
  5. 2026Robust Multi-Source Covid-19 Detection in CT Imagescited by 0
  6. 2026Evidence-Based Actor-Verifier Reasoning for Echocardiographic Agentscited by 0
  7. 2026Weight Group-wise Post-Training Quantization for Medical Foundation Modelcited by 0