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无CT减弱校正¹³N-氨心脏PET图像,使用有条件无声扩散隐性模型与对数线性规范化

Hao Sun¹, Xiaotong Hong¹, Yijun Lu²

¹School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH‑1211 Geneva 4, Switzerland; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.

Computer methods and programs in biomedicine

|December 9, 2025

概括

这项研究引入了一种新的AI模型,即有条件否定扩散隐性模型 (cDDIM),用于创建准确的心脏PET图像,而无需额外的CT扫描辐射. 与传统方法相比,cDDIM模型,特别是对数线性规范化 (cDDIM_LLN) 的cDDIM模型显示出更高的性能.

科学领域:

医疗成像医学成像
人工智能的人工智能
核医学是一种核医学.

背景情况:

基于CT的衰减校正 (CT-AC) 在心脏PET中增加了辐射暴露,对儿科患者和需要重复扫描的患者构成风险.
开发无CT减弱校正方法对于降低心脏PET成像中的辐射剂量至关重要.

研究的目的:

开发和评估一种新型的条件否定扩散隐性模型 (cDDIM),用于生成无CT减弱校正的心脏PET图像.
通过使用定量指标,评估cDDIM与传统的基于生成对抗网络 (GAN) 的方法的性能.

主要方法:

使用2.5DcDDIM框架,使用非减弱校正 (NAC) PET输入来合成CT-AC图像.
测试了两个cDDIM版本:cDDIM与对数线性规范化 (cDDIM_LLN) 和cDDIM与线性规范化 (cDDIM_LN).
在两个中心的数据上,使用正常化平均平方误差 (NMSE) 和细分绝对百分比误差 (APE) 评估了性能.

主要成果:

无论是cDDIM_LLN还是cDDIM_LN,在生成减弱校正的心脏PET图像方面,它们都在视觉上优于基于GAN的方法.
与cDDIM_LN和基于GAN的方法相比,cDDIM_LLN在两个中心都表现出明显较低的NMSE和APE.
cDDIM_LN还显示了比基于GAN的方法更好的APE.

结论:

基于cDDIM的方法成功合成了与临床CT-AC可比的心脏PET标记物分布.
在评估的方法中,cDDIM_LLN方法获得了最佳性能.
基于cDDIM的减弱校正比传统的基于GAN的技术有了显著的进步,提供了一个有前途的无CT解决方案.

关键词:

减弱校正的纠正减弱校正的纠正心脏PET可以使用.深度学习是一种深度学习.消灭扩散隐性模型的隐性模型

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