{"ID":2824034,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.24260","arxiv_id":"2512.24260","title":"Physically-Grounded Manifold Projection Model for Generalizable Metal Artifact Reduction in Dental CBCT","abstract":"Metal artifacts in Dental CBCT severely obscure anatomical structures, hindering diagnosis. Current deep learning for Metal Artifact Reduction (MAR) faces limitations: supervised methods suffer from spectral blurring due to \"regression-to-the-mean\", while unsupervised ones risk structural hallucinations. Denoising Diffusion Models (DDPMs) offer realism but rely on slow, stochastic iterative sampling, unsuitable for clinical use. To resolve this, we propose the Physically-Grounded Manifold Projection (PGMP) framework. First, our Anatomically-Adaptive Physics Simulation (AAPS) pipeline synthesizes high-fidelity training pairs via Monte Carlo spectral modeling and patient-specific digital twins, bridging the synthetic-to-real gap. Second, our DMP-Former adapts the Direct x-Prediction paradigm, reformulating restoration as a deterministic manifold projection to recover clean anatomy in a single forward pass, eliminating stochastic sampling. Finally, a Semantic-Structural Alignment (SSA) module anchors the solution using priors from medical foundation models (MedDINOv3), ensuring clinical plausibility. Experiments on synthetic and multi-center clinical datasets show PGMP outperforms state-of-the-art methods on unseen anatomy, setting new benchmarks in efficiency and diagnostic reliability. Code and data: https://github.com/ricoleehduu/PGMP.","short_abstract":"Metal artifacts in Dental CBCT severely obscure anatomical structures, hindering diagnosis. Current deep learning for Metal Artifact Reduction (MAR) faces limitations: supervised methods suffer from spectral blurring due to \"regression-to-the-mean\", while unsupervised ones risk structural hallucinations. Denoising Diff...","url_abs":"https://arxiv.org/abs/2512.24260","url_pdf":"https://arxiv.org/pdf/2512.24260v2","authors":"[\"Zhi Li\",\"Yaqi Wang\",\"Bingtao Ma\",\"Yifan Zhang\",\"Huiyu Zhou\",\"Shuai Wang\"]","published":"2025-12-30T14:36:26Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false,"code_links":[{"ID":605546,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2824034,"paper_url":"https://arxiv.org/abs/2512.24260","paper_title":"Physically-Grounded Manifold Projection Model for Generalizable Metal Artifact Reduction in Dental CBCT","repo_url":"https://github.com/ricoleehduu/PGMP","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}