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 describes the mathematical structure of the ill-posed nonlinearinverse problem of low-dose dental cone-beam computed tomography (CBCT) andexplains the advantages of a deep learning-based approach to the reconstructionof computed tomography images over conventional regularization methods. Thispaper explains the underlying reasons why dental CBCT is more ill-posed thanstandard computed tomography. Despite this severe ill-posedness, the demand fordental CBCT systems is rapidly growing because of their cost competitivenessand low radiation dose. We then describe the limitations of existing methods inthe accurate restoration of the morphological structures of teeth using dentalCBCT data severely damaged by metal implants. We further discuss the usefulnessof panoramic images generated from CBCT data for accurate tooth segmentation.We also discuss the possibility of utilizing radiation-free intra-oral scandata as prior information in CBCT image reconstruction to compensate for thedamage to data caused by metal implants.

Nonlinear ill-posed problem in low-dose dental cone-beam computed tomography