Evaluation of a Metal Artifact Reduction Algorithm and an Optimization Filter in the Estimation of Peri-implant Dehiscence Defects by Using Cone Beam Computed Tomography: an In-vitro Study

Objectives: The aim of this study was to assess the effect of a metal artifact reduction (MAR) algorithm and the adaptive image noise optimizer (AINO) optimization filter in the detection of peri-implant dehiscences with cone beam computed tomography (CBCT).

Study Design: Nine implants (3 zirconium, 3 titanium, and 3 zirconium-titanium) were placed in 3 sheep heads. Dehiscences were created on the buccal and lingual/palatal surfaces. A total of 9 defects and 9 controls with no defects were evaluated by 3 observers. Each sheep head was scanned 5 times with 4 scan modes; (1) without MAR/without AINO; (2) with MAR/without AINO; (3) without MAR/with AINO; and (4) with MAR/with AINO. Receiver operating characteristic analysis and weighted kappa coefficients were used to calculate diagnostic efficacy and intra- and interobserver agreements for each implant type and scan mode.

Results: For all implant types, dehiscences were most accurately detected when both MAR and AINO were applied (P ≤ .045). Detection of dehiscences was more accurate with titanium implants (P ≤ .040). There were no significant differences in agreement among and between the observers.

Conclusions: The use of both MAR and AINO enhanced the detection accuracy of artificially created dehiscences in proximity to implants. Their combined use is recommended for detecting peri-implant dehiscences.