Dental CT metal artefact reduction based on sequential substitution

Objective: Metal artefacts can seriously degrade the visual quality and interpretability of dental CT images. Existing image processing algorithms for metal artefact reduction (MAR) are either too computationally expensive to be used in clinical scanners or effective only in correcting mild artefacts. The aim of the present study was to investigate whether it is possible to improve the efficacy of the computationally efficient projection-correction approach to MAR by exploiting the spatial dependency or autocorrelation between adjacent CT slices.Methods: A new projection-correction algorithm [MAR by sequential substitution (MARSS)] was developed based on the idea that the corrupted portions of the projection data can be substituted with the corresponding portions from an unaffected adjacent slice. The performance of MARSS was evaluated relative to the projection-correction method of Watzke and Kalendar using a two-alternative forced choice (2AFC) visual trial involving 20 observers and 20 clinical CT data sets.¹⁶Results: The Cochran Q test revealed no significant difference in the responses across all observers. The data were then pooled and analysed using a one-tailed exact binomial test. This revealed that the proportion of responses in favour of MARSS was significant (P < 2.2 × 10⁻¹⁶). A second Cochran Q test revealed no significant difference in the responses across all images.Conclusions: It is possible to improve the efficacy of projection correction by exploiting spatial autocorrelation. The 2AFC results suggest that the proposed MARSS algorithm outperforms competing computationally efficient algorithms in terms of reducing metal artefacts whilst at the same time preserving/revealing anatomic detail.