Evaluation of metal artefact reduction in cone-beam computed tomography images of different dental materials

Abstract

Dental materials of high atomic number and density are widely used in dentistry and can produce artefact that compromise CBCT image. The present study demonstrated that metal artefact reduction algorithm is an effective tool to improve image quality.

Cone beam computed tomography in Endodontics – a review of the literature

“…Metal artefact reduction algorithms (MAR) are becoming more common in operating and viewing software in order to overcome this disadvantage (Queiroz et al . 2018a). These are mathematical post‐processing programmes which analyse an area of image where streak, beam hardening and photon starvation artefact are found, and analyse the adjacent ‘normal’ image to deduce what grey shades should be found in the boundary zones immediately adjacent to the artefact.…”

“…, Queiroz et al . 2018a). The efficacy of the programmes is further improved when the offending metal object is in the centre of the field of view (Queiroz et al .…”

Ability to detect endodontic complications using three different cone beam computed tomography units with and without artefact reduction modes: an ex vivo study

“…Moreover, a previous study that assessed artefact reduction on CBCT images taken using different imaging parameters showed that the efficacy of artefact reduction was similar in CBCT images obtained with different FOV and voxel sizes (Queiroz et al . ). Further research is essential in order to clarify the effects of FOV, artefact reduction modes, CBCT units and settings on the diagnosis of endodontic complications.…”

Use of the metal artefact reduction tool in the identification of fractured endodontic instruments in cone‐beam computed tomography

“…It can be hypothesized that the action of the MAR tool was not effective in the present study, because only a small number of artefacts were created due to the small dimensions of the instrument (2 mm). In the same way, the lack of an improvement in their detection in filled canals can be attributed to the composition of gutta‐percha (mixture of zinc oxide and isoprene rubber), which has a low atomic number (Queiroz et al ), and thus does not generate sufficient artefacts to be significantly reduced by the MAR tool (Freitas et al , Queiroz et al ). Therefore, it can be inferred that the detection of fractured instruments in filled canals is difficult not because of the direct effect of the artefacts, but because the gutta‐percha and endodontic instruments produce a similar density on the final images that does not allow the root filling and instrument to be differentiated.…”

“…Although there are doubts about the effectiveness of the MAR tool, it is suggested that this tool works in the post‐processing stage, correcting highly dense or sparse regions in the image, that is, acting by applying a threshold in relation to the grey value average in the image and increasing the contrast‐to‐noise ratio (Bechara et al , Queiroz et al , Freitas et al ). The image noise was quantified by measuring the standard deviation of mean grey value (Freitas et al , Queiroz et al ). High standard deviation means high image noise, which is supposed to be well discerned in homogeneous structures.…”