# A two-stage approach for beam hardening artifact reduction in low-dose dental CBCT

arXiv: https://arxiv.org/abs/2010.03778 · HTML: https://mlsanity.com/paper/2010.03778/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

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

- [Nonlinear ill-posed problem in low-dose dental cone-beam computed tomography](https://mlsanity.com/paper/2303.01678/) (2023)
- [SinoSynth: A Physics-based Domain Randomization Approach for Generalizable CBCT Image Enhancement](https://mlsanity.com/paper/2409.18355/) (2024)
- [Metal Artifact Reduction with Intra-Oral Scan Data for 3D Low Dose Maxillofacial CBCT Modeling](https://mlsanity.com/paper/2202.03571/) (2022)
- [Prior Frequency Guided Diffusion Model for Limited Angle (LA)-CBCT Reconstruction](https://mlsanity.com/paper/2404.01448/) (2024)
- [EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis](https://mlsanity.com/paper/2605.20470/) (2026)

## Abstract (arXiv)

This paper presents a two-stage method for beam hardening artifact correction of dental cone beam computerized tomography (CBCT). The proposed artifact reduction method is designed to improve the quality of maxillofacial imaging, where soft tissue details are not required. Compared to standard CT, the additional difficulty of dental CBCT comes from the problems caused by offset detector, FOV truncation, and low signal-to-noise ratio due to low X-ray irradiation. To address these problems, the proposed method primarily performs a sinogram adjustment in the direction of enhancing data consistency, considering the situation according to the FOV truncation and offset detector. This sinogram correction algorithm significantly reduces beam hardening artifacts caused by high-density materials such as teeth, bones, and metal implants, while tending to amplify special types of noise. To suppress such noise, a deep convolutional neural network is complementarily used, where CT images adjusted by the sinogram correction are used as the input of the neural network. Numerous experiments validate that the proposed method successfully reduces beam hardening artifacts and, in particular, has the advantage of improving the image quality of teeth, associated with maxillofacial CBCT imaging.