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    科学领域:

    • 神经成像是一种神经成像.
    • 放射化学 放射化学是指辐射化学.
    • 人工智能的人工智能

    背景情况:

    • 位子发射断层扫描 (PET) 测量标记物结合的量化依赖于准确的输入函数 (IF) 估计.
    • 对IF的动脉采样具有侵入性和技术挑战性,限制了临床适用性.

    研究的目的:

    • 开发和验证一种基于神经网络的非侵入性框架,用于从动态PET数据中估计IF.
    • 评估框架在不同数据集和扫描仪中的通用性.

    主要方法:

    • 一个修补的变异自编码器 (pVAE) 用于减小维度以产生不确定性的IF (NNIF-dPET).
    • 该框架与使用图像衍生输入函数 (NNIF-IDIF) 和未经校正的血液信号 (NNIF-unBlood) 的方法进行了比较.
    • 分布量 (VT) 是使用神经网络生成的IFs的平均输出信号来计算的.

    主要成果:

    • 该NNIF-dPET方法的准确性与传统的动脉IF相美.
    • NNIF-dPET的性能优于依赖图像衍生输入函数的方法.
    • 隐藏空间表示有效地对母血IF估计的全血活性进行了近似.

    结论:

    • 开发的神经网络框架使可扩展和非侵入性PET量化成为可能.
    • 这种方法具有显著的潜力,可以使先进的PET成像得到更广泛的临床采用.
    • 准确的IF估计可以在没有侵入性动脉采样的情况下实现.