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Research Paper#Medical Imaging, Deep Learning, Metal Artifact Reduction🔬 ResearchAnalyzed: Jan 3, 2026 15:42

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

Published:Dec 30, 2025 14:36
1 min read
ArXiv

Analysis

This paper addresses the critical problem of metal artifacts in dental CBCT, which hinder diagnosis. It proposes a novel framework, PGMP, to overcome limitations of existing methods like spectral blurring and structural hallucinations. The use of a physics-based simulation (AAPS), a deterministic manifold projection (DMP-Former), and semantic-structural alignment with foundation models (SSA) are key innovations. The paper claims superior performance on both synthetic and clinical datasets, setting new benchmarks in efficiency and diagnostic reliability. The availability of code and data is a plus.
Reference

“PGMP framework outperforms state-of-the-art methods on unseen anatomy, setting new benchmarks in efficiency and diagnostic reliability.”

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Research#medical imaging🔬 ResearchAnalyzed: Jan 4, 2026 10:01

GANeXt: A Fully ConvNeXt-Enhanced Generative Adversarial Network for MRI- and CBCT-to-CT Synthesis

Published:Dec 22, 2025 12:32
1 min read
ArXiv

Analysis

This article introduces GANeXt, a novel generative adversarial network (GAN) architecture. The core innovation lies in the integration of ConvNeXt, a convolutional neural network architecture, to improve the synthesis of CT images from MRI and CBCT scans. The research likely focuses on enhancing image quality and potentially reducing radiation exposure by synthesizing CT scans from alternative imaging modalities. The use of ArXiv suggests this is a preliminary research paper, and further peer review and validation would be needed to assess the practical impact.
Reference

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PermalinkArXiv