生物标志物 生物标志物
Yiming Che1,2, Ziqi Guo3, Jay Shah1,2
1Arizona State University, Tempe, AZ, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
一个新的Cycle-GAN模型使用未配对的训练数据协调神经成像数据,在选择最小的配对数据的情况下显著改善结果. 这种方法提高了PET成像分析的数据通用性.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 医疗数据分析 医学数据分析
背景情况:
- 协调正子发射断层扫描 (PET) 成像数据,如匹兹堡化合物-B (PiB) 和氧糖-PET (FDG-PET),对于准确的分析至关重要.
- 传统的协调方法通常需要大量的配对数据,这些数据可能很难或不可能获得.
- 现有的未配对方法可能缺乏复杂医疗数据集所需的通用性.
研究的目的:
- 为神经成像数据开发和验证一种基于Cycle-GAN的新型协调模型.
- 为了消除在模型训练期间需要大量配对数据的需求,使用未配对数据代替.
- 提高PET成像数据协调的普遍性和性能.
主要方法:
- 一个循环-GAN模型适用于表式神经成像数据,修改生成器和区分器使用多层感知子 (MLP) 和跳过连接.
- 该模型使用大量未配对的PiB和FBP测量数据集进行训练 (可能FBP是另一种PET追踪器或成像模式).
- 一小部分配对数据用于模型选择,通过皮质平均标准化摄取值比率 (mcSUVR) 的皮尔森相关性来评估性能.
主要成果:
- 选择的循环-GAN模型在协调的PiB数据和实际的PiB数据之间实现了高的皮尔森相关性 (0.85),超过了基线.
- 统计分析 (施泰格的Z测试) 证实了与基线方法相比的显著改善 (p < 0.0001).
- 添加了诸如CL和人口统计数据 (年龄,性别) 等额外的特征,进一步改善了协调结果.
结论:
- 一个基于Cycle-GAN的协调模型成功地开发了未配对的神经成像数据.
- 该模型展示了培训协调模型的可行性,其中主要是未配对的数据,只需要一个小的子集进行选择.
- 在PiB和FBP测量方面取得了有希望的协调结果,这表明在神经成像分析中更广泛的应用潜力.
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