Effect of auto-adaptive metal artifact reduction (aMAR) program in cone-beam computed tomography on assessing pre-implant bone levels

TL;DR

This study introduces an auto-adaptive metal artifact reduction algorithm in CBCT to improve the accuracy of pre-implant bone level assessments.

Contribution

The novel contribution is the implementation of an aMAR program in CBCT for better peri-implant bone evaluation.

Findings

Metallic artifacts hinder accurate CBCT imaging of dental implants.

The aMAR program enhances the dynamic range for more precise bone level assessments.

aMAR improves the detection of peri-implant structures in CBCT scans.

Abstract

This research aimed to introduce an auto-adaptive metal artifact reduction (aMAR) algorithm in cone-beam computed tomography (CBCT) to assess the levels of the pre-implant alveolar crest. Dental implants as a treatment modality for edentulous patients consist of a titanium alloy, which creates a metal artifact, resulting in a dark dental structure in the CBCT scans. Metallic artifacts are limiting factors for the precise detection in CBCT images. These are related to the dark areas around materials and metallic structures (e.g., restorations, implants, and endodontic instruments). To overcome this problem, the metal artifact reduction (MAR) program has been recommended as a post-procedure stage for CBCT image reconstruction. Recent developments offer CBCT scanners with an aMAR option with a greater dynamic range to help overcome the challenges of peri-implant bone evaluation to reach accurate dental diagnoses.