Metal Artifact Reduction (MAR) is one of the notorious problems in dental CT imaging. The presence of metallic implants often introduces severe metal artifacts in the reconstructed CT images, which obstruct the visualization of dental structures. This highly ill-posed problem has been addressed with the rise of artificial intelligence in respect to deep learning. However, majority of the approaches rely on the supervision of large paired dataset, which is often infeasible in the clinical practice. In this work, we present NeMAR, a Neural fields-based Metal Artifact Reduction method for dental CT. NeMAR leverages coordinate-based neural representation along with two key components: the masked loss and the regularization loss. These elements synergistically empower the neural fields to generate metal-artifact-reduced CT image with high fidelity. Notably, NeMAR requires only the original metal-artifact-corrupted image as an input, thus eliminating the need for extensive paired data. The validation using simulated dental CT datasets demonstrates the effectiveness of NeMAR in accurately recovering the shape of dental structures. In essence, NeMAR presents a promising data-free approach to enhance dental CT imaging.
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