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

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.