生物标志物 生物标志物
Channelle Tham1, Harrison Aduluwa2, Ethan Draper3
1Radboud University, Nijmegen, Netherlands.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
概括
这项研究展示了一种基于云的,具有成本效益的工具,用于分析动脉脊柱标记 (ASL) 的MRI数据,提高脑血流 (CBF) 研究在资源有限的环境中的可重复性.
科学领域:
- 神经成像是一种神经成像.
- 医学物理 医学物理
- 放射学 放射学是一门学科.
背景情况:
- 动脉脊柱标记 (ASL) 是一种非侵入性的MRI技术,用于评估大脑血流 (CBF).
- 在资源有限的环境 (RCS) 中,ASL数据分析的障碍包括缺乏专业知识和高计算成本.
- ASL-MRICloud提供了一个基于云的开源解决方案,用于ASL处理,适合RCS.
研究的目的:
- 在Google Colab中实现ASL-MRICloud,以实现可扩展和成本效益的ASL数据分析.
- 提高RCS人口研究的可复制性.
- 通过CONNEXIN计划培训非洲研究人员进行神经图像分析.
主要方法:
- 在Google Colab中实现了ASL-MRICloud,用于自动化ASL处理和CBF量化.
- 输血成像ASL (OSIPI-ASL) MRI挑战数据集的开放科学倡议被用于初始测试和验证.
- 该管道由四个CONNEXIN团队进一步测试,分析了PREVENT-AD研究中的数据.
主要成果:
- 来自OSIPI-ASL数据集的初步结果可用 (图1).
- 管道,文档和团队分析结果将在protocol.io.io上公开分享.
结论:
- 该研究评估了利用模拟和现实数据进行可复制ASL数据分析的资源高效工具的可行性.
- 该方法将被分享,以便在RCS中更广泛地采用,以促进包容和可重复的成像研究.
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