n combined PET/MR, attenuation correction (AC) is performed indirectly based on the available MR image information. Metal implant-induced susceptibility artifacts and subsequent signal voids challenge MR-based AC. Several papers acknowledge the problem in PET attenuation correction when dental artifacts are ignored, but none of them attempts to solve the problem. We propose a clinically feasible correction method which combines Active Shape Models (ASM) and k- Nearest-Neighbors (kNN) into a simple approach which finds and corrects the dental artifacts within the surface boundaries of the patient anatomy. ASM is used to locate a number of landmarks in the T1-weighted MR-image of a new patient. We calculate a vector of offsets from each voxel within a signal void to each of the landmarks. We then use kNN to classify each voxel as belonging to an artifact or an actual signal void using this offset vector, and fill the artifact voxels with a value representing soft tissue. We tested the method using fourteen patients without artifacts, and eighteen patients with dental artifacts of varying sizes within the anatomical surface of the head/neck region. Though the method wrongly filled a small volume in the bottom part of a maxillary sinus in two patients without any artifacts, due to their abnormal location, it succeeded in filling all dental artifact regions in all patients. In conclusion, we propose a method, which combines ASM and kNN into a simple approach which, as the results show, succeeds to find and correct the dental artifacts within the anatomical surface.