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

TL;DR

This paper introduces a two-stage correction method combining sinogram adjustment and deep learning to effectively reduce beam hardening artifacts in low-dose dental CBCT, enhancing maxillofacial image quality.

Contribution

It proposes a novel two-stage approach that integrates sinogram correction with deep neural networks for artifact reduction in dental CBCT imaging.

Findings

Significant reduction of beam hardening artifacts in dental CBCT images.

Improved visualization of teeth and maxillofacial structures.

Enhanced image quality despite low-dose imaging conditions.

Abstract

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.