发表机构
University of Peradeniya（佩拉德尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对牙科 CBCT 中金属伪影问题，提出用微调的循环一致对抗网络进行无监督金属伪影减少，利用未配对数据集，集成特定架构，经实验其在多项指标上有提升且实现实时效率，为牙科植入物成像提供了高保真解决方案。
AI 中文摘要
牙科植入物产生的金属伪影会显著降低锥形束计算机断层扫描（CBCT）的体积质量，影响关键解剖结构的观察并降低诊断精度。为此，提出了一种无监督深度学习框架用于金属伪影减少（MAR），利用针对高保真恢复优化的循环一致对抗网络（CycleGAN）。该方法利用从公共 ToothFairy 数据集中挑选的约 4000 张图像的未配对数据集，其架构集成了基于 U-Net 的生成器和 PatchGAN 鉴别器。定量基准测试表明，在保留的测试集上，盲/无参考图像空间质量评估器（BRISQUE）得分提高了 34.6%，弗雷歇因距离（FID）从 207.03 大幅降至 157.04，结构相似性指数测量（SSIM）为 0.9105，且每切片推理时间为 3.03 毫秒，实现实时效率。专家验证确认了高保真度，但建议在人工监督下将该架构作为临床决策支持工具，以确保极端情况下的可靠性。本研究通过可扩展软件管道提高诊断清晰度，为高保真牙科植入物成像提供了强大解决方案。
英文摘要
Metal artifacts generated by dental implants significantly degrade cone-beam computed tomography (CBCT) volumes, obscuring critical anatomical structures and compromising diagnostic precision. To address this, an unsupervised deep learning framework has been proposed for Metal Artifact Reduction (MAR) utilizing a Cycle-Consistent Adversarial Network (CycleGAN) optimized for high-fidelity restoration. Unlike supervised methods that rely on unattainable voxel-aligned paired datasets, the proposed approach leverages an unpaired dataset of approximately 4,000 images, curated from the public ToothFairy dataset. The architecture integrates U-Net-based generators and PatchGAN discriminators, specifically tuned to mitigate generative hallucinations and preserve morphological integrity. Quantitative benchmarking on a held-out test set demonstrates a 34.6\% improvement in the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score, a substantial reduction in Fréchet Inception Distance (FID) from 207.03 to 157.04, and a superior Structural Similarity Index Measure (SSIM) of 0.9105. The framework achieves real-time efficiency with a 3.03 ms inference time per slice, effectively suppressing artifacts while preserving anatomical detail. Expert validation confirms high fidelity; however, to ensure reliability in extreme cases, the architecture is recommended as a clinical decision-support tool under human-in-the-loop oversight. By enhancing diagnostic clarity via a scalable software pipeline, this study provides a robust solution for high-fidelity dental implant imaging.
Commentsaccepted and published work
Journal refChamika, T.; Dhanapala, S.N.A.; Nimalaweera, S.; Dissanayake, M.B.; Jayasinghe, R.D. Unsupervised Metal Artifact Reduction in Dental CBCT Using Fine-Tuned Cycle-Consistent Adversarial Networks. Digital 2026, 6, 31
