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开发和评估贝叶斯优化FDG基于人口的输入函数,用于在临床实践中实施参数成像

Alessia Artesani¹, Lorenzo Leonardi², Jelena Jandric²

¹Humanitas University, Via Rita Levi Montalcini 4, Milan, 20089, ITALY.

Biomedical physics & engineering express

|May 12, 2025

概括

这项研究引入了一种新的贝叶斯优化方法,用于创建动态PET扫描的标准化输入函数,改善瘤诊断和治疗评估准确性. 这种方法确保了在不同中心和协议之间可靠的参数成像.

科学领域:

核医学是一种核医学.
医疗成像医学成像
生物物理学的生物物理.

背景情况:

动态正子发射断层扫描 (PET) 对于瘤诊断和治疗反应评估至关重要.
不一致的输入函数 (IF) 生成方法导致不可靠的参数成像描述器.
需要一个标准化的IF来提高基于PET的诊断的一致性和可靠性.

研究的目的:

开发一种超参数优化方法来推导FDG PET.基于人口的输入函数 (PBIF).
创建一个独立于特定获取和注入协议的IF方法.
提高PET诊断中的参数成像的可靠性和标准化.

主要方法:

利用贝叶斯超参数优化来建模患者特定的图像衍生输入函数 (IDIF).
从十名患者的下降大动脉中提取和正常化IDIF.
集成注射配置文件使用双矩形模型用于标记剂注射和残留冲洗.

主要成果:

贝叶斯优化成功地以高精度 (R2 = 0.97) 建模了患者的IDIF.
估计的注射和冲洗参数与记录的数据保持一致,显示出较低的变化.
在使用PBIF时,帕特拉克分析表明它与注射特征无关.

结论:

贝叶斯优化使得强大的PBIF建模能够在没有先前了解注入细节的情况下实现.
这些发现支持统一的FDG PBIF,促进了PET中心的参数成像标准化.
这种方法的进步是多中心验证和更广泛的临床应用的基础.

关键词:

贝叶斯式优化贝叶斯式优化德国联邦财政总局PET 输入函数建模输入函数建模参数成像是一种参数成像技术.帕特拉克分析分析

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