What Drives Metal Artifacts in CBCT? A Comparative Study of Detector Types and Metallic Object Configurations

One major limitation of cone beam computed tomography (CBCT) systems is the presence of metallic artifacts. Given the widespread use of metals in dental treatments and the diversity of CBCT designs, this study aimed to compare the effect of aligned and lateral offset detectors on metal artifact formation within the field of view (FOV).

This in vitro cross-sectional study evaluated four CBCT systems-two with aligned and two with lateral offset detectors. A dry human skull phantom was scanned with various metallic objects (a titanium implant, a tooth with a chrome-cobalt post and PFM crown, and a tooth with an amalgam restoration) placed in the FOV in two orientations (same vs. opposite side of the region of interest [ROI]) and in three quantities (1, 2, or 3 objects). A polypropylene tube filled with dipotassium phosphate solution was placed in the right first premolar socket to simulate alveolar bone. Ten axial slices were analyzed using 1.5-mm circular ROIs. Mean gray value (MGV) and standard deviation (SD) were calculated, where higher MGV and lower SD indicate lower artifact severity. Data were analyzed with four-way weighted analysis of variance (ANOVA), and multiple comparisons were performed using Games-Howell, Bonferroni, and t-tests (p < 0.05).

Detector type, material, object location, and number significantly affected artifact formation. Aligned detectors showed significantly higher MGV and lower SD, indicating reduced artifact severity (p < 0.001). Amalgam produced the most severe artifacts, reflected by the lowest MGVs, while cobalt-chromium yielded the highest variability (SD) (p < 0.005). Artifacts were more severe when metallic objects were on the same side as the ROI and increased with more objects. Several significant interaction effects were observed (p < 0.05).

Detector type, metal composition, and the number and position of metallic objects within the FOV significantly influence artifact formation in CBCT images. Optimizing these factors can reduce artifacts and improve image quality.