Deep learning-based metal artifact reduction methods often struggle to apply the trained network to real data due to the difference between simulated training and real application datasets. To solve this problem, we propose a patient-specific data augmentation method that can effectively train the convolutional neural network of metal artifact reduction with a single patient dental CT volume. The idea of the proposed method is to generate the dataset using metal-unaffected slices from the patient data that require metal artifact reduction. The dataset generated by leveraging the adjacency of both metal-affected and metal-unaffected slices closely resembles real metal artifact images in terms of teeth shape and CT system geometry. To overcome the problem of small data size due to using only a single patient's data, we augment the data by generating various patient-specific metal masks. We segment the bone and label each tooth to get the size and position of the tooth. We determine the size and shape of the metal objects in the metal mask based on the information of the labeled teeth. Metal artifacts are simulated from metal masks and patient data using a polychromatic sinogram simulation method and iterative estimation of metal attenuation coefficients. For experiments, we train the same U-net structure network with different train datasets and tested with real metal artifact. The results show that the patientspecific dataset of the proposed method is more suitable for reducing the real metal artifact than the dataset generated using large amounts of data.
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.
In dental CT, the presence of metal objects introduces various artifacts caused by photon starvation and beam hardening. Although several metal artifacts reduction methods have been proposed, they still have limitations in terms of reducing the metal artifacts. In this work, we proposed a method to reduce the metal artifacts with convolutional neural network (CNN). The proposed method is comprised of two steps. In STEP 1, we acquired a more accurate prior image, which is used in normalized metal artifact reduction (NMAR) technique through the CNN. The metal artifacts in output image from STEP 1 are reduced by CNN training, which provides more accurate prior images. In STEP 2, the NMAR is conducted with the acquired prior image from CNN result. To validate the proposed method, we used dental CT images containing metals and without metal to evaluate that the proposed method could significantly reduce the metal artifacts compared to the NMAR method.
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