Physically-Grounded Manifold Projection Model for Generalizable Metal Artifact Reduction in Dental CBCT

• URL: http://arxiv.org/abs/2512.24260v2
• Date: Thu, 01 Jan 2026 03:28:35 GMT
• Title: Physically-Grounded Manifold Projection Model for Generalizable Metal Artifact Reduction in Dental CBCT
• Authors: Zhi Li, Yaqi Wang, Bingtao Ma, Yifan Zhang, Huiyu Zhou, Shuai Wang,
• Abstract summary: Metal artifacts in Dental CBCT severely obscure anatomical structures.<n>Current deep learning for Metal Artifact Reduction (MAR) faces limitations.<n>Denoising Diffusion Models (DDPMs) offer realism but rely on slow, iterative sampling.
• Score: 20.637726557566793
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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