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. 2018 Jul;47(5):20180013.
doi: 10.1259/dmfr.20180013. Epub 2018 Mar 22.

An ex vivo study of automated motion artefact correction and the impact on cone beam CT image quality and interpretability

Affiliations

An ex vivo study of automated motion artefact correction and the impact on cone beam CT image quality and interpretability

Rubens Spin-Neto et al. Dentomaxillofac Radiol. 2018 Jul.

Abstract

Objectives: To assess the impact of head motion artefacts and an automated artefact-correction system on cone beam CT (CBCT) image quality and interpretability for simulated diagnostic tasks.

Methods: A partially dentate human skull was mounted on a robot simulating four types of head movement (anteroposterior translation, nodding, lateral rotation, and tremor), at three distances (0.75, 1.5, and 3 mm) based on two movement patterns (skull returning/not returning to the initial position). Two diagnostic tasks were simulated: dental implant planning and detection of a periapical lesion. Three CBCT units were used to examine the skull during the movements and no-motion (control): Cranex 3Dx (CRA), Orthophos SL 3D (ORT), and X1 without (X1wo) and with (X1wi) an automated motion artefact-correction system. For each diagnostic task, 88 examinations were performed. Three observers, blinded to unit and movement, scored image quality: presence of stripe artefacts (present/absent), overall unsharpness (present/absent), and image interpretability (interpretable/non-interpretable). κ statistics assessed interobserver agreement, and descriptive statistics summarized the findings.

Results: Interobserver agreement for image interpretability was good (average κ = 0.68). Regarding dental implant planning, X1wi images were interpretable by all observers, while for the other units mainly the cases with tremor were non-interpretable. Regarding detection of a periapical lesion, besides tremor, most of the 3 mm movements based on the "not returning" pattern were also non-interpretable for CRA, ORT, and X1wo. For X1wi, two observers scored 1.5 mm tremor and one observer scored 3 mm tremor as non-interpretable.

Conclusions: The automated motion artefact-correction system significantly enhanced CBCT image quality and interpretability.

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Figures

Figure 1.
Figure 1.
Experimental setup. (a) The partially dentate human skull embedded in wax integrated with the robot. The arrow points at the dotted plate attached to the unit, but isolated from the skull. (b) The skull is equipped with a headband containing the plastic, dotted plate that is tracked by three dedicated optical cameras. (c) Detail of the three dedicated optical cameras, while capturing images of the dotted plate during the full examination to determine the orientation and position of the skull.
Figure 2.
Figure 2.
Representative axial, sagittal, and cross-sectional images regarding implant planning, and considering the four CBCT image-acquisition setups (CRA, ORT, X1wo, and X1wi). The first four rows show control images with no movement. The four rows in the middle show images acquired with a 3 mm nodding movement. The last four rows show images acquired with a 3 mm tremor movement. CBCT, cone beam CT; CRA, Cranex 3Dx; ORT, Orthophos SL 3D; X1wi,X1 with; X1wo, X1 without.
Figure 3.
Figure 3.
Representative axial, sagittal, and cross-sectional images regarding diagnosis of apical periodontitis, and considering the four CBCT image-acquisition setups (CRA, ORT, X1wo, and X1wi). The first four rows show control images with no movement. The four rows in the middle show images acquired with a 3 mm nodding movement. The last four rows show images acquired with a 3 mm tremor movement. CBCT, cone beam CT; CRA, Cranex 3Dx; ORT, Orthophos SL 3D; X1wi, X1 with; X1wo, X1 without.

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