English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a two-stage method for beam hardening artifact correctionof dental cone beam computerized tomography (CBCT). The proposed artifactreduction method is designed to improve the quality of maxillofacial imaging,where soft tissue details are not required. Compared to standard CT, theadditional difficulty of dental CBCT comes from the problems caused by offsetdetector, FOV truncation, and low signal-to-noise ratio due to low X-rayirradiation. To address these problems, the proposed method primarily performsa 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 hardeningartifacts caused by high-density materials such as teeth, bones, and metalimplants, while tending to amplify special types of noise. To suppress suchnoise, a deep convolutional neural network is complementarily used, where CTimages adjusted by the sinogram correction are used as the input of the neuralnetwork. Numerous experiments validate that the proposed method successfullyreduces beam hardening artifacts and, in particular, has the advantage ofimproving the image quality of teeth, associated with maxillofacial CBCTimaging.

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