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用有条件潜伏扩散模型的金属工件减少算法用于牙圆束CT.

Da-In Choi¹, Sungho Yun¹, Subong Hyun¹

¹Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.

Journal of applied clinical medical physics

|October 24, 2025

概括

这项研究引入了一种新方法,使用隐性扩散模型来减少牙圆束计算机断层扫描 (CBCT) 中的金属工件,改善图像质量以进行更好的诊断.

关键词:

圆光束CT CT 的情况.潜在的扩散模型.金属工艺品金属工艺品减少金属工件的减少

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科学领域:

医疗成像医学成像
放射学中的人工智能
计算成像技术的成像

背景情况:

计算机断层扫描 (CT) 中的金属工件对准确的诊断和治疗计划提出了重大挑战.
现有的金属工件减少 (MAR) 技术在不同程度的成功下,在sinogram,投影或图像领域运行.

研究的目的:

开发一种先进的金属工件减少 (MAR) 技术,用于牙科圆束计算机断层扫描 (CBCT).
为了利用潜在扩散模型 (LDM) 来减少工件,并增强正常化金属工件减少 (NMAR) 方案.

主要方法:

使用隐性扩散模型 (LDM) 来生成金属工件减少的图像,条件是损坏的CBCT图像.
通过使用平均平方误差 (MSE) 和学习感知图像补丁相似性 (LPIPS) 的联合目标函数来训练LDM.
修改后的正常化金属工件减少 (NMAR) 过程,包括自动金属细分网络和二次工件校正网络,被用作缓解幻觉等生成模型工件的先验.

主要成果:

拟议的LDM增强的NMAR方法显著优于经典的NMAR和基于最先进的卷积神经网络的MAR (CNNMAR) 方法.
与CNNMAR.相比,图像质量的改善被量化为RMSE (34.78×10−4到19.30×10−4) 的减少,PSNR (49.354.3) 和SSIM (91.297.2) 的增加.
在牙科CBCT中成功的临床实施证明了有效的金属工件减少,同时保留了关键的解剖结构.

结论:

开发的方法为减少牙科CBCT中的金属工件提供了实用和有效的解决方案.
这一进步具有显著的潜力,可以提高牙科的诊断准确性和治疗规划.