Impact of motion artefacts and motion‐artefact correction on diagnostic accuracy of apical periodontitis in CBCT images: an ex vivo study in human cadavers

Abstract

AimTo assess the impact of motion artefacts and motion‐artefact correction on diagnostic accuracy of apical periodontitis (AP) in CBCT images.MethodologyBased on clinical and radiographic inspection of 40 formalin‐fixated human jaw specimens, 77 roots in 45 teeth (molars and premolars), with various disease and treatment state, were selected. The specimens were mounted on a robot simulating 3‐mm movement types (nodding, lateral rotation and tremor). CBCT images with and without (controls) movements were acquired in four CBCT units: without motion‐artefact correction in Cranex 3Dx, Orthophos SL 3D, and Promax 3D Mid, and with motion‐artefact correction in Promax 3D Mid and X1. Three observers blindly assessed (i) whether the images were interpretable and (ii) if AP was present (5‐step probability index). Histopathology provided the reference standard for presence of AP. Weighted Kappa statistics described inter‐observer agreement. Estimates of diagnostic accuracy were assessed by means of receiver operator characteristic (ROC) curve analysis. Area under the curve (AUC) provided a measure of accuracy, and paired‐sample AUC difference tests compared differences amongst the CBCT units and movement types.ResultsObserver agreement was substantial for control images, moderate for motion‐artefact corrected images and fair for images without motion‐artefact correction. When movement was present, motion‐artefact correction reduced the percentage of images scored as noninterpretable or with uncertain disease state (score 3 in the 5‐step probability index). Control images were not perfectly accurate (both false‐positive and false‐negative results were present; AUC 0.750–0.799). Images acquired with movement and without motion‐artefact correction (AUC 0.541–0.709) were associated with significantly lower accuracy than control images (P < 0.05). With motion‐artefact correction, accuracy was comparable to that observed in control images (AUC 0.732–0.790).ConclusionsDiagnostic accuracy of apical periodontitis in CBCT images was dependent on the presence of motion artefacts (i.e. lower accuracy associated with the presence of movement). Motion‐artefact correction systems positively influenced image interpretability and diagnostic accuracy.

Endodontic working length measurements of premolars and molars in high-resolution dental MRI: a clinical pilot study for assessment of reliability and accuracy

“…In contrast, available information on the accuracy and reliability of CBCT in endodontic working length measurements for premolar/molars is restricted predominantly to ex vivo studies [ 29 , 30 , 36 , 37 ]. This is problematic because ex vivo studies exclude motion artifacts, which are typical under clinical conditions with possible impact on diagnostic accuracy [ 38 ]. Liang et al and Connert et al included 162 and 42 extracted teeth, respectively, of which 46/42 and 12/12 were premolars/molars, respectively.…”

Present status and future directions: vertical root fractures in root filled teeth

“…Due to their lower atomic number and reduced radiodensity, fibre (non‐metallic) posts have less impact on the diagnostic accuracy when compared to metal posts (Marinho Vieira et al, 2020 et al, 2020; Figure 2). Recent advances in image reconstruction such as motion correction (Spin‐Neto et al, 2020) and metal artefact reduction algorithms (MAR) (Queiroz et al, 2018) have been used to reduce imaging artefact, and therefore, improve diagnostic accuracy. However, the results of these studies do not appear to improve the diagnosis of VRFs in root filled teeth (Bechara et al, 2013; Fontenele et al, 2021).…”

Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images

“…To comprehensively understand artifacts formation mechanism, researchers did a plenty of valuable work. Spin-Neto et al [ 3 ] has placed a strong emphasis on explaining the physics behind the occurrence of motion artifacts with the aim of aiding artifact detection and mitigation in clinical situations. The impact and the correction of motion artifacts on diagnostic accuracy of apical periodontitis in CBCT images have also been demonstrated in [ 3 ].…”

“…Spin-Neto et al [ 3 ] has placed a strong emphasis on explaining the physics behind the occurrence of motion artifacts with the aim of aiding artifact detection and mitigation in clinical situations. The impact and the correction of motion artifacts on diagnostic accuracy of apical periodontitis in CBCT images have also been demonstrated in [ 3 ]. To build dental implant artifact detection algorithm, seven designated features were extracted from ROIs (regions of interest) and machine learning with random forests was used in [ 4 ].…”