# X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1905.06902 · HTML: https://mlsanity.com/paper/1905.06902/

## Key facts

- Cited by 85 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 30 of 85)

- [NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction](https://mlsanity.com/paper/2108.10991/) (2021)
- [NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2209.14540/) (2022)
- [Structure-Aware Sparse-View X-ray 3D Reconstruction](https://mlsanity.com/paper/2311.10959/) (2023)
- [R2-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction](https://mlsanity.com/paper/2405.20693/) (2024)
- [MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray](https://mlsanity.com/paper/2202.01020/) (2022)
- [Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2303.06681/) (2023)
- [Perspective Projection-Based 3D CT Reconstruction from Biplanar X-rays](https://mlsanity.com/paper/2303.05297/) (2023)
- [Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction](https://mlsanity.com/paper/2407.01090/) (2024)
- [Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data](https://mlsanity.com/paper/2201.09318/) (2022)
- [C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2406.03902/) (2024)
- [Self-Supervised 2D/3D Registration for X-Ray to CT Image Fusion](https://mlsanity.com/paper/2210.07611/) (2022)
- [Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2303.14739/) (2023)
- [A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction](https://mlsanity.com/paper/2105.11692/) (2021)
- [DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rays](https://mlsanity.com/paper/2407.13545/) (2024)
- [CT Reconstruction from Few Planar X-rays with Application towards Low-resource Radiotherapy](https://mlsanity.com/paper/2308.02100/) (2023)
- [DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rays](https://mlsanity.com/paper/2408.15118/) (2024)
- [DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or Mono-planar 2D X-ray(s)](https://mlsanity.com/paper/2409.08850/) (2024)
- [Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers](https://mlsanity.com/paper/2308.14152/) (2023)
- [Neural Radiance Fields in Medical Imaging: A Survey](https://mlsanity.com/paper/2402.17797/) (2024)
- [IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration](https://mlsanity.com/paper/2111.00484/) (2021)
- [DeepCA: Deep Learning-based 3D Coronary Artery Tree Reconstruction from Two 2D Non-simultaneous X-ray Angiography Projections](https://mlsanity.com/paper/2407.14616/) (2024)
- [XTransCT: Ultra-Fast Volumetric CT Reconstruction using Two Orthogonal X-Ray Projections for Image-guided Radiation Therapy via a Transformer Network](https://mlsanity.com/paper/2305.19621/) (2023)
- [Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems](https://mlsanity.com/paper/2308.08511/) (2023)
- [3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning](https://mlsanity.com/paper/2405.10705/) (2024)
- [DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2505.02628/) (2025)
- [DVG-Diffusion: Dual-View Guided Diffusion Model for CT Reconstruction from X-Rays](https://mlsanity.com/paper/2503.17804/) (2025)
- [Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images](https://mlsanity.com/paper/2109.11480/) (2021)
- [Improving Computed Tomography (CT) Reconstruction via 3D Shape Induction](https://mlsanity.com/paper/2208.10937/) (2022)
- [Oral-3Dv2: 3D Oral Reconstruction from Panoramic X-Ray Imaging with Implicit Neural Representation](https://mlsanity.com/paper/2303.12123/) (2023)
- [NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic Radiographs](https://mlsanity.com/paper/2304.04027/) (2023)

## Abstract (arXiv)

Computed tomography (CT) can provide a 3D view of the patient's internal organs, facilitating disease diagnosis, but it incurs more radiation dose to a patient and a CT scanner is much more cost prohibitive than an X-ray machine too. Traditional CT reconstruction methods require hundreds of X-ray projections through a full rotational scan of the body, which cannot be performed on a typical X-ray machine. In this work, we propose to reconstruct CT from two orthogonal X-rays using the generative adversarial network (GAN) framework. A specially designed generator network is exploited to increase data dimension from 2D (X-rays) to 3D (CT), which is not addressed in previous research of GAN. A novel feature fusion method is proposed to combine information from two X-rays.The mean squared error (MSE) loss and adversarial loss are combined to train the generator, resulting in a high-quality CT volume both visually and quantitatively. Extensive experiments on a publicly available chest CT dataset demonstrate the effectiveness of the proposed method. It could be a nice enhancement of a low-cost X-ray machine to provide physicians a CT-like 3D volume in several niche applications.