Abstract
Dental amalgams are a common source of artifacts in head and neck (HN) images. Commercial artifact reduction techniques have been offered, but are substantially ineffectual at reducing artifacts from dental amalgams, can produce additional artifacts, provide inaccurate HU information, or require extensive computation time, and thus offer limited clinically utility. The goal of this work was to define and validate a novel algorithm and provide a phantom-based testing as proof of principle. An initial clinical comparison to a vendor's current solution was also performed. The algorithm uses two-angled CT scans in order to generate a single image set with minimal artifacts posterior to the metal implants. The algorithm was evaluated using a phantom simulating a HN patient with dental fillings. Baseline (no artifacts) geometrical measurements of the phantom were taken in the anterior-posterior, left-right, and superior-inferior directions and compared to the metal-corrected images using our algorithm to evaluate possible distortion from application of the algorithm. Mean HU numbers were also compared between the baseline scan and corrected image sets. A similar analysis was performed on the vendor's algorithm for comparison. The algorithm developed in this work successfully preserved the image geometry and HU and corrected the CT metal artifacts in the region posterior to the metal. The average total distortion for all gantry angles in the AP, LR, and SI directions was 0.17, 0.12, and 0.14 mm, respectively. The HU measurements showed significant consistency throughout the different reconstructed images when compared to the baseline image sets. The vendor's algorithm also showed no geometrical distortion but performed inferiorly in the HU number analysis compared to our technique. Our novel metal artifact management algorithm, using CT gantry angle tilts, provides a promising technique for clinical management of metal artifacts from dental amalgam.