Deep Learning-based Metal Extraction with No Reference in Dental CBCT for Metal Artifact Reduction

Metal artifact reduction (MAR) is crucial for improving the quality of dental cone-beam computed tomography (CBCT) images. However, inaccurate extraction of metal in MAR can lead to incomplete suppression of metal artifacts, obscuring adjacent bone structures in resultant images. The pre-existing metal artifacts such as streaks, shadowing, and cupping complicate the extraction of metallic segments. In particular, dental CBCT applications are vulnerable to metal artifacts due to the lack of anti-scatterers and the frequent appearance of metallic restorations. Conventional image processing methods for metal extraction have some limitations, for example, relying on predefined parameters and requiring manual interventions. To overcome these challenges, we introduce a deep learning-based metal extraction method in an unsupervised manner, eliminating the labor-intensive label annotation process. This method combines several methods, including unpaired image-to-image translation, weakly supervised learning, and geometric transformations, followed by supervised learning for real-time metal extraction. As a result, the proposed method achieves high-quality metal extraction with an accuracy of 90%, as evaluated with manual annotations, and shows a significant improvement in MAR performance for clinical CBCT images compared to our conventional method.