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Cone beam CT scans with and without artefact reduction in root fracture detection of endodontically treated teeth.

ABSTRACT: OBJECTIVES: Cone beam CT (CBCT) is used widely to depict root fracture (RF) in endodontically treated teeth. Beam hardening and other artefacts due to gutta-percha may increase the time of the diagnosis and result in an incorrect diagnosis. Two CBCT machines, ProMax(®) (Planmeca, Helsinki, Finland) and Master 3D(®) (Vatech, Hwaseong, Republic of Korea), have the option of applying an artefact reduction (AR) algorithm. The aim of this study was to determine whether using an AR algorithm in two CBCT machines enhances the accuracy of detecting RFs in endodontically treated teeth. METHODS: 66 roots were collected and decoronated. All roots were treated endodontically using the same technique with gutta-percha and zinc oxide cement. One-half of the roots were randomly selected and fractured using a nail that was tapped gently with a hammer until complete fracture resulted in two root fragments; the two root fragments were glued together with one layer of methyl methacrylate. The roots were placed randomly in eight prepared beef rib fragments. RESULTS: The highest accuracy was obtained when the ProMax was used without AR. The lowest accuracy was obtained with the Master 3D when used with AR. For both machines, accuracy was significantly higher without AR than with AR. Both with and without AR, the ProMax machine was significantly more accurate than the Master 3D machine. The same rank ordering was obtained for both sensitivity and specificity. CONCLUSIONS: For both machines, AR decreased the accuracy of RF detection in endodontically treated teeth. The highest accuracy was obtained when using the ProMax without AR.

SUBMITTER: Bechara B

PROVIDER: S-EPMC3635776 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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Project description:ObjectivesThe susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate attenuation correction in PET/CT imaging. This study investigates the potential of deep learning-based metal artefact reduction (MAR) in quantitative PET/CT imaging.MethodsDeep learning-based metal artefact reduction approaches were implemented in the image (DLI-MAR) and projection (DLP-MAR) domains. The proposed algorithms were quantitatively compared to the normalized MAR (NMAR) method using simulated and clinical studies. Eighty metal-free CT images were employed for simulation of metal artefact as well as training and evaluation of the aforementioned MAR approaches. Thirty 18F-FDG PET/CT images affected by the presence of metallic implants were retrospectively employed for clinical assessment of the MAR techniques.ResultsThe evaluation of MAR techniques on the simulation dataset demonstrated the superior performance of the DLI-MAR approach (structural similarity (SSIM) = 0.95 ± 0.2 compared to 0.94 ± 0.2 and 0.93 ± 0.3 obtained using DLP-MAR and NMAR, respectively) in minimizing metal artefacts in CT images. The presence of metallic artefacts in CT images or PET attenuation correction maps led to quantitative bias, image artefacts and under- and overestimation of scatter correction of PET images. The DLI-MAR technique led to a quantitative PET bias of 1.3 ± 3% compared to 10.5 ± 6% without MAR and 3.2 ± 0.5% achieved by NMAR.ConclusionThe DLI-MAR technique was able to reduce the adverse effects of metal artefacts on PET images through the generation of accurate attenuation maps from corrupted CT images.Key points• The presence of metallic objects, such as dental implants, gives rise to severe photon starvation, beam hardening and scattering, thus leading to adverse artefacts in reconstructed CT images. • The aim of this work is to develop and evaluate a deep learning-based MAR to improve CT-based attenuation and scatter correction in PET/CT imaging. • Deep learning-based MAR in the image (DLI-MAR) domain outperformed its counterpart implemented in the projection (DLP-MAR) domain. The DLI-MAR approach minimized the adverse impact of metal artefacts on whole-body PET images through generating accurate attenuation maps from corrupted CT images.

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Cone beam CT scans with and without artefact reduction in root fracture detection of endodontically treated teeth.

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