# Perspective Projection-Based 3D CT Reconstruction from Biplanar X-rays

arXiv: https://arxiv.org/abs/2303.05297 · HTML: https://mlsanity.com/paper/2303.05297/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks](https://mlsanity.com/paper/1905.06902/) (2019)
- [MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray](https://mlsanity.com/paper/2202.01020/) (2022)
- [End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images](https://mlsanity.com/paper/2004.00871/) (2020)
- [Deep Back Projection for Sparse-View CT Reconstruction](https://mlsanity.com/paper/1807.02370/) (2018)
- [Single-image Tomography: 3D Volumes from 2D Cranial X-Rays](https://mlsanity.com/paper/1710.04867/) (2017)

## Built on by (most cited first, 18 of 18)

- [Learning 3D Gaussians for Extremely Sparse-View Cone-Beam CT Reconstruction](https://mlsanity.com/paper/2407.01090/) (2024)
- [C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2406.03902/) (2024)
- [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)
- [Neural Radiance Fields in Medical Imaging: A Survey](https://mlsanity.com/paper/2402.17797/) (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)
- [Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling](https://mlsanity.com/paper/2406.18422/) (2024)
- [SPIDER: Structure-Preferential Implicit Deep Network for Biplanar X-ray Reconstruction](https://mlsanity.com/paper/2507.04684/) (2025)
- [SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model](https://mlsanity.com/paper/2507.05148/) (2025)
- [Shape-from-Template with Generalised Camera](https://mlsanity.com/paper/2508.13791/) (2025)
- [LayersReg: A Layer-by-Layer Progressive Regressor for Reliable Intraoperative 3D/2D Registration](https://mlsanity.com/paper/2606.26647/) (2026)
- [3D Path Planning for Robot-assisted Vertebroplasty from Arbitrary Bi-plane X-ray via Differentiable Rendering](https://mlsanity.com/paper/2512.05803/) (2025)
- [XDen-1K: A Density Field Dataset of Real-World Objects](https://mlsanity.com/paper/2512.10668/) (2025)
- [DuFal: Dual-Frequency-Aware Learning for High-Fidelity Extremely Sparse-view CBCT Reconstruction](https://mlsanity.com/paper/2601.15416/) (2026)
- [ILV: Iterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction](https://mlsanity.com/paper/2603.14915/) (2026)
- [Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays](https://mlsanity.com/paper/2603.26509/) (2026)
- [From Sparse X-rays to 3D CT: Training-Free Reconstruction with Diffusion Priors](https://mlsanity.com/paper/2606.20763/) (2026)

## Abstract (arXiv)

X-ray computed tomography (CT) is one of the most common imaging techniques used to diagnose various diseases in the medical field. Its high contrast sensitivity and spatial resolution allow the physician to observe details of body parts such as bones, soft tissue, blood vessels, etc. As it involves potentially harmful radiation exposure to patients and surgeons, however, reconstructing 3D CT volume from perpendicular 2D X-ray images is considered a promising alternative, thanks to its lower radiation risk and better accessibility. This is highly challenging though, since it requires reconstruction of 3D anatomical information from 2D images with limited views, where all the information is overlapped. In this paper, we propose PerX2CT, a novel CT reconstruction framework from X-ray that reflects the perspective projection scheme. Our proposed method provides a different combination of features for each coordinate which implicitly allows the model to obtain information about the 3D location. We reveal the potential to reconstruct the selected part of CT with high resolution by properly using the coordinate-wise local and global features. Our approach shows potential for use in clinical applications with low computational complexity and fast inference time, demonstrating superior performance than baselines in multiple evaluation metrics.