# Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review

arXiv: https://arxiv.org/abs/2403.18565 · HTML: https://mlsanity.com/paper/2403.18565/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 143 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 30 of 30)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2201.11793/) (2022)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Solving Inverse Problems in Medical Imaging with Score-Based Generative Models](https://mlsanity.com/paper/2111.08005/) (2021)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1711.10561/) (2017)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy](https://mlsanity.com/paper/2303.16320/) (2023)
- [Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2303.06681/) (2023)
- [Deep learning-based synthetic-CT generation in radiotherapy and PET: a review](https://mlsanity.com/paper/2102.02734/) (2021)
- [Learning with Known Operators reduces Maximum Training Error Bounds](https://mlsanity.com/paper/1907.01992/) (2019)
- [PYRO-NN: Python Reconstruction Operators in Neural Networks](https://mlsanity.com/paper/1904.13342/) (2019)
- [Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction](https://mlsanity.com/paper/2008.01567/) (2020)
- [A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance](https://mlsanity.com/paper/2008.06262/) (2020)
- [Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation Using Physics-Based Data Augmentation](https://mlsanity.com/paper/2103.05690/) (2021)
- [Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery](https://mlsanity.com/paper/2108.13844/) (2021)
- [Deep Encoder-decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-view Data](https://mlsanity.com/paper/1911.05880/) (2019)
- [Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks](https://mlsanity.com/paper/2104.12510/) (2021)
- [Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction](https://mlsanity.com/paper/1810.03999/) (2018)
- [4D X-Ray CT Reconstruction using Multi-Slice Fusion](https://mlsanity.com/paper/1906.06601/) (2019)
- [A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks](https://mlsanity.com/paper/2010.00421/) (2020)
- [Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal](https://mlsanity.com/paper/1906.06854/) (2019)
- [Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories](https://mlsanity.com/paper/1909.08868/) (2019)

## Built on by (most cited first, 5 of 5)

- [NeAS: 3D Reconstruction from X-ray Images using Neural Attenuation Surface](https://mlsanity.com/paper/2503.07491/) (2025)
- [Physical foundations for trustworthy medical imaging: a review for artificial intelligence researchers](https://mlsanity.com/paper/2505.02843/) (2025)
- [MInDI-3D: Iterative Deep Learning in 3D for Sparse-view Cone Beam Computed Tomography](https://mlsanity.com/paper/2508.09616/) (2025)
- [Unsupervised Metal Artifact Reduction in Dental CBCT using Fine-tuned Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/2607.20977/) (2026)
- [Dual-domain U-Nets with embedded back projection operators for motion-resolved 4D CBCT reconstruction](https://mlsanity.com/paper/2608.03430/) (2026)

## Abstract (arXiv)

Deep learning based approaches have been used to improve image quality in cone-beam computed tomography (CBCT), a medical imaging technique often used in applications such as image-guided radiation therapy, implant dentistry or orthopaedics. In particular, while deep learning methods have been applied to reduce various types of CBCT image artifacts arising from motion, metal objects, or low-dose acquisition, a comprehensive review summarizing the successes and shortcomings of these approaches, with a primary focus on the type of artifacts rather than the architecture of neural networks, is lacking in the literature. In this review, the data generation and simulation pipelines, and artifact reduction techniques are specifically investigated for each type of artifact. We provide an overview of deep learning techniques that have successfully been shown to reduce artifacts in 3D, as well as in time-resolved (4D) CBCT through the use of projection- and/or volume-domain optimizations, or by introducing neural networks directly within the CBCT reconstruction algorithms. Research gaps are identified to suggest avenues for future exploration. One of the key findings of this work is an observed trend towards the use of generative models including GANs and score-based or diffusion models, accompanied with the need for more diverse and open training datasets and simulations.