Evaluation of a metal artifact reduction algorithm and an adaptive image noise optimization filter in the estimation of peri-implant fenestration defects using cone beam computed tomography: an in-vitro study.

Abstract

The aim of this study is to assess the effects of metal artifact reduction (MAR) and adaptive image noise enhancer (AINO) in CBCT imaging on the detection accuracy of artificially created fenestration defects in proximity to titanium and zirconium implants in sheep jaw. Six zirconium and 10 titanium implants were planted on mandibular jaws of three sheep, and artificial defects were created. All images were obtained with a standard voxel size (0.150 mm3) and 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 during CBCT scanning. A total of 60 CBCT scans were produced. For all types of implants, intra- and inter-observer kappa values were the highest for MAR filter. The scan mode of with MAR filter was found to have the highest area under the curve (AUC), whereas the scan mode of without both MAR and AINO filters was found to have the lowest AUC values with statistical significance (p ≤ 0.05). Titanium implants were found to have higher AUC values than zirconium (p ≤ 0.05). Both MAR module and AINO filters enhance the accuracy of the detection of peri-implant fenestrations; however, the use of MAR filter solely can be recommended for detection of peri-implant fenestrations.