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Objectives: To evaluate different materials in simulating soft tissues and to analyze the influence of these materials on the mean (MPIV) and standard deviation of pixel intensity values comparing them to a gold-standard in CBCT images., Methods: Images of three piglet heads with their soft tissues intact (gold-standard) and different simulant materials were acquired: ice, modelling wax, and ballistic gelatin, with the same thickness of the original soft tissues. The pixel intensities were measured in dental, bone and soft tissues regions, in the mandible and maxilla, for all the groups. Analysis of variance, Dunnet's, Pearson's and linear regression tests were performed., Results: The simulators did not significantly change the MPIV of teeth in comparison with the gold-standard ( p = 0.1017). Only ice ( p = 0.0156) affected the MPIV of bone. Wax ( p  = 0.001) and ice ( p = 0.0076), but not ballistic gelatin ( p = 0.5814), altered the MPIV of soft tissue regions. When assessing the influence of the location (mandible or maxilla) among the simulants, the differences were significant only for the soft tissue regions. Standard deviation was not influenced by simulants ( p > 0.05), but ballistic gelatin presented the lower variability., Conclusions: The ballistic gelatin was the best soft tissue simulant since it had the lowest influence on the pixel intensity values for all regions.

Objectives: To evaluate the action of a metal artefact reduction (MAR) tool when artefact-generator metal object is at different positions in the field of view (FOV)., Methods: A cylindrical utility wax phantom, with a metal alloy sample inside, was made. The phantom was positioned centrally and peripherally in the FOV for image acquisition, with and without the MAR tool activation. The standard deviation values (image noise levels) from areas around the metal sample and the control area were obtained. The numbers were compared by Student's t-test (α = 0.05)., Results: When the tool was activated, a significant difference of image noise was observed for central and peripheral positioning, for both control area (p = 0.0012) and metal area (p = 0.03), and a smaller level of noise was observed for images with phantoms in central positioning. A decrease in image noise with the tool activated was found only in phantoms with the metal object positioned centrally in the FOV., Conclusions: For the MAR tool to be effective, the artefact-generator object needs to be in the central region of the FOV.