# MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray

arXiv: https://arxiv.org/abs/2202.01020 · HTML: https://mlsanity.com/paper/2202.01020/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation](https://mlsanity.com/paper/2003.13659/) (2020)
- [AutoInt: Automatic Integration for Fast Neural Volume Rendering](https://mlsanity.com/paper/2012.01714/) (2020)
- [Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis](https://mlsanity.com/paper/2101.04775/) (2021)
- [X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks](https://mlsanity.com/paper/1905.06902/) (2019)
- [Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting](https://mlsanity.com/paper/2010.01942/) (2020)

## Built on by (most cited first, 30 of 66)

- [3D Gaussian Splatting as Markov Chain Monte Carlo](https://mlsanity.com/paper/2404.09591/) (2024)
- [Implicit Neural Representation in Medical Imaging: A Comparative Survey](https://mlsanity.com/paper/2307.16142/) (2023)
- [Structure-Aware Sparse-View X-ray 3D Reconstruction](https://mlsanity.com/paper/2311.10959/) (2023)
- [Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis](https://mlsanity.com/paper/2403.04116/) (2024)
- [Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues](https://mlsanity.com/paper/2305.19906/) (2023)
- [CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution](https://mlsanity.com/paper/2303.16242/) (2023)
- [Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging](https://mlsanity.com/paper/2301.10520/) (2023)
- [BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance Fields](https://mlsanity.com/paper/2306.03000/) (2023)
- [Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane](https://mlsanity.com/paper/2312.15253/) (2023)
- [Perspective Projection-Based 3D CT Reconstruction from Biplanar X-rays](https://mlsanity.com/paper/2303.05297/) (2023)
- [Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields](https://mlsanity.com/paper/2211.12285/) (2022)
- [Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2303.14739/) (2023)
- [Neural Radiance Fields for the Real World: A Survey](https://mlsanity.com/paper/2501.13104/) (2025)
- [UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields](https://mlsanity.com/paper/2311.05836/) (2023)
- [HyperNeRFGAN: Hypernetwork approach to 3D NeRF GAN](https://mlsanity.com/paper/2301.11631/) (2023)
- [Fluid Dynamics Network: Topology-Agnostic 4D Reconstruction via Fluid Dynamics Priors](https://mlsanity.com/paper/2303.09871/) (2023)
- [Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers](https://mlsanity.com/paper/2308.14152/) (2023)
- [Radar Fields: An Extension of Radiance Fields to SAR](https://mlsanity.com/paper/2312.12961/) (2023)
- [Neural Radiance Fields in Medical Imaging: A Survey](https://mlsanity.com/paper/2402.17797/) (2024)
- [TOGS: Gaussian Splatting with Temporal Opacity Offset for Real-Time 4D DSA Rendering](https://mlsanity.com/paper/2403.19586/) (2024)
- [NeRF-US: Removing Ultrasound Imaging Artifacts from Neural Radiance Fields in the Wild](https://mlsanity.com/paper/2408.10258/) (2024)
- [GaSpCT: Gaussian Splatting for Novel CT Projection View Synthesis](https://mlsanity.com/paper/2404.03126/) (2024)
- [Neural Light Spheres for Implicit Image Stitching and View Synthesis](https://mlsanity.com/paper/2409.17924/) (2024)
- [DWTNeRF: Boosting Few-shot Neural Radiance Fields via Discrete Wavelet Transform](https://mlsanity.com/paper/2501.12637/) (2025)
- [DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rays](https://mlsanity.com/paper/2503.17804/) (2025)
- [Oral-3Dv2: 3D Oral Reconstruction from Panoramic X-Ray Imaging with Implicit Neural Representation](https://mlsanity.com/paper/2303.12123/) (2023)
- [Evaluating the point cloud of individual trees generated from images based on Neural Radiance fields (NeRF) method](https://mlsanity.com/paper/2312.03372/) (2023)
- [UlRe-NeRF: 3D Ultrasound Imaging through Neural Rendering with Ultrasound Reflection Direction Parameterization](https://mlsanity.com/paper/2408.00860/) (2024)
- [Estimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans](https://mlsanity.com/paper/2409.09387/) (2024)
- [InnerGS: Internal Scenes Reconstruction and Segmentation via Factorized 3D Gaussian Splatting](https://mlsanity.com/paper/2508.13287/) (2025)

## Abstract (arXiv)

Computed tomography (CT) is an effective medical imaging modality, widely used in the field of clinical medicine for the diagnosis of various pathologies. Advances in Multidetector CT imaging technology have enabled additional functionalities, including generation of thin slice multiplanar cross-sectional body imaging and 3D reconstructions. However, this involves patients being exposed to a considerable dose of ionising radiation. Excessive ionising radiation can lead to deterministic and harmful effects on the body. This paper proposes a Deep Learning model that learns to reconstruct CT projections from a few or even a single-view X-ray. This is based on a novel architecture that builds from neural radiance fields, which learns a continuous representation of CT scans by disentangling the shape and volumetric depth of surface and internal anatomical structures from 2D images. Our model is trained on chest and knee datasets, and we demonstrate qualitative and quantitative high-fidelity renderings and compare our approach to other recent radiance field-based methods. Our code and link to our datasets are available at https://github.com/abrilcf/mednerf