UNSTRUCTURED
This study aimed to assess the efficacy of metal artifact reduction (MAR) algorithm of two cone-beam computed tomography (CBCT) systems for detection of peri-implant fenestration and dehiscence.
Thirty-six titanium implants were placed in bone blocks of bovine ribs. Fenestration and dehiscence were created in the buccal bone around implants using a round bur. The bone blocks were then mounted in a wax rim to simulate the mandible. CBCT images were obtained using Cranex 3D and ProMax 3D CBCT systems with and without MAR algorithm before and after creation of defects. Two experienced radiologists observed the images twice with a 2-week interval. Data were analyzed using SPSS software version 22.The Kappa coefficient of agreement, the area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, accuracy of different imaging modalities were calculated and analyzed.
According to the kappa statistics, the intra- and inter-observer agreements were higher for images without the MAR algorithm compared with those with the MAR algorithm. In both CBCT systems, use of MAR algorithm decreased the area under the ROC curve and subsequently the diagnostic accuracy for detection of fenestration and dehiscence. The sensitivity, specificity and accuracy of both CBCT systems were higher in absence of the MAR algorithm. The specificity of ProMax 3D for detection of fenestration was equal with/without the MAR algorithm.
Although CBCT is suitable for detection of peri-implant defects, application of the MAR algorithm does not enhance the detection of peri-implant fenestration and dehiscence.
Metal artifact reduction in computed tomography images based on developed generative adversarial neural network
