The efficacy of metal artifact reduction (MAR) algorithm in cone‐beam computed tomography on the diagnostic accuracy of fenestration and dehiscence around dental implants

Abstract

Background The aim of the present study was to investigate the impact of metal artifact reduction (MAR) algorithm of cone‐beam computed tomography (CBCT) on the diagnostic accuracy of fenestration and dehiscence around dental implants. Methods Twenty‐three dental implants were placed adjacent to the dehiscence and 23 adjacent to the fenestration defects on bovine bone blocks. The blocks were scanned with CBCT unit in two modes, with and without MAR algorithm. The area under the receiver operator characteristic (ROC) curves (Az value), specificity, sensitivity, positive predictive value, negative predictive value, and accuracy were determined for all modes. Results For both defects, the Az values were higher in off MAR condition. The values of sensitivity, positive predictive value, negative predictive value, and accuracy, were higher in off MAR condition for both defects. However, the specificity in both defects in the two modes was equal. Conclusion The MAR algorithm in CBCT unit may not be helpful in increasing the diagnostic accuracy of fenestration and dehiscence around dental implants.

Efficacy of Metal Artifact Reduction Algorithm of Cone-Beam Computed Tomography for Detection of Fenestration and Dehiscence around Dental Implants

“…The area under the curve was 0.959 without the MAR and 0.707 with the MAR algorithm, respectively, for the control group. These findings indicate that in general, the area under the curve and subsequently the diagnostic accuracy for detection of fenestration was higher than that for the detection of dehiscence that was in accordance with the results of Sheikhi et al [ 16 ] and Azevedo-Vaz et al [ 2 ]. According to the study by Azevedo-Vaz et al [ 2 ], the detection of a dehiscence defect with only one inferior border is more difficult than detection of a fenestration defect with one superior and one inferior border [ 2 ].…”

“…CBCT images obtained by the ProMax 3D and Cranex 3D CBCT systems can correctly detect the presence or absence of these defects by over 61% and 70%, respectively. This finding was in line with that of Sheikhi et al that CBCT images had a sensitivity and specificity higher than 0.79 for detection of fenestration and dehiscence [16]; although the CBCT systems used in the two studies were different.…”

“…The only exception was related to the specificity; in addition, PPV of ProMax 3D for detection of fenestration was similar in with and without the MAR algorithm. This reduction in sensitivity and specificity may be due to the fact that the MAR algorithm not only eliminates the image artifacts but also removes some useful gray levels that are beneficial for diagnosis [ 18 ] that this result was in line with that of Sheikhi et al [ 16 ] and Azevedo-Vaz et al [ 2 ]. In the study by Sheikhi et al [ 16 ], the values were higher when the MAR algorithm was not used.…”

“…This reduction in sensitivity and specificity may be due to the fact that the MAR algorithm not only eliminates the image artifacts but also removes some useful gray levels that are beneficial for diagnosis [ 18 ] that this result was in line with that of Sheikhi et al [ 16 ] and Azevedo-Vaz et al [ 2 ]. In the study by Sheikhi et al [ 16 ], the values were higher when the MAR algorithm was not used. Also, Azevedo-Vaz et al [ 2 ] reported higher sensitivity, specificity, PPV and NPV for both defects when the MAR algorithm was not applied.…”

Efficacy of metal artifact reduction algorithm of cone-beam computed tomography for detection of fenestration and dehiscence around dental implants (Preprint)

“…The area under the curve was 0.959 without the MAR and 0.707 with the MAR algorithm, respectively, for the control group. These find- ings indicate that in general, the area under the curve and subsequently the diagnostic accuracy for detection of fenestration was higher than that for the detection of dehiscence that was in accordance with the results of Sheikhi et al [16] and Azevedo-Vaz et al [2]. According to the study by Azevedo-Vaz et al [2], the detection of a dehiscence defect with only one inferior border is more difficult than detection of a fenestration defect with one superior and one inferior border [2].…”