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.