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Found 6 papers, 0 papers with code
Neural Representation-Based Method for Metal-induced Artifact Reduction in Dental CBCT Imaging
This study introduces a novel reconstruction method for dental cone-beam computed tomography (CBCT), focusing on effectively reducing metal-induced artifacts commonly encountered in the presence of prevalent metallic implants.
Automatic 3D Registration of Dental CBCT and Face Scan Data using 2D Projection Images
A main contribution of this study is that the proposed method does not require annotated training data of facial landmarks because it uses a pre-trained facial landmark detection algorithm that is known to be robust and generalized to various 2D face image models.
A robust multi-domain network for short-scanning amyloid PET reconstruction
Notably, for external validation datasets from unseen domains, the proposed method achieved comparable or superior results relative to methods trained with these datasets, in terms of quantitative metrics such as normalized root mean-square error and structure similarity index measure.
Nonlinear ill-posed problem in low-dose dental cone-beam computed tomography
This paper describes the mathematical structure of the ill-posed nonlinear inverse problem of low-dose dental cone-beam computed tomography (CBCT) and explains the advantages of a deep learning-based approach to the reconstruction of computed tomography images over conventional regularization methods.
Metal Artifact Reduction with Intra-Oral Scan Data for 3D Low Dose Maxillofacial CBCT Modeling
To improve the learning ability, the proposed network is designed to take advantage of the intra-oral scan data as side-inputs and perform multi-task learning of auxiliary tooth segmentation.
Unpaired image denoising using a generative adversarial network in X-ray CT
This paper proposes a deep learning-based denoising method for noisy low-dose computerized tomography (CT) images in the absence of paired training data.
