Normalized metal artifact reduction in head and neck computed tomography.

OBJECTIVE

Artifacts from dental hardware affect image quality and the visualization of lesions in the oral cavity and oropharynx in computed tomography (CT). Therefore, magnetic resonance imaging is considered the imaging modality of choice in this region. Standard methods for metal artifact reduction (MAR) in CT replace the metal-affected raw data by interpolation, which is prone to new artifacts. We developed a generalized normalization technique for MAR (NMAR) that aims to suppress algorithm-induced artifacts and validated the performance of this algorithm in a clinical trial.

MATERIAL AND METHODS

A 3-dimensional forward projection identifies the metal-affected raw data in the original projections after metal is segmented in the image domain by thresholding. A prior image is used to normalize the projections before interpolation. The original raw data are divided pixel-wise by the projection data of the prior image and, after interpolation, are denormalized again. Data from 19 consecutive patients with metal artifacts from dental hardware were reconstructed with standard filtered backprojection (FBP), linear interpolation MAR (LIMAR), and NMAR. The image quality of slices containing metal was analyzed for the severity of artifacts and diagnostic value; magnetic resonance imaging performed the same day on a 3-T system served as a reference standard in all cases.

RESULTS

A total of 260 slices containing metal dental hardware were analyzed. A total of 164 slices were nondiagnostic with FBP, 157 slices with LIMAR, and 87 slices with NMAR. The mean (SD) number of slices per patient with severe artifacts was 10.1 (3.7), 9.6 (4.6), and 5.4 (3.6) and the mean (SD) number of slices with artifacts affecting diagnostic confidence was 3.3 (1.7), 4.9 (2.9), and 3.7 (1.9) for FBP, LIMAR, and NMAR, respectively (P < 0.001). Pairwise comparison did not show significant differences between FBP and LIMAR (P = 0.40), but there were significant differences between FBP and NMAR as well as LIMAR and NMAR (both P < 0.001). Interobserver agreement was excellent (κ = 0.974). Two malignant lesions were unmasked with NMAR image reconstructions. No algorithm-related artifacts were detected in regions that did not contain metal in NMAR images.

CONCLUSION

Normalized MAR has the potential to improve image quality in patients with artifacts from dental hardware and to improve the diagnostic accuracy of CT of the oral cavity and oropharynx.