Microstructure.jl:用于与扩散MRI相适应的概率微结构模型的Julia包
Ting Gong1, Anastasia Yendiki1
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.
Imaging neuroscience (Cambridge, Mass.)
|January 26, 2026
概括
Microstructure.jl是一个新的Julia包,用于从MRI数据中估计组织微结构. 它提供灵活的建模和强大的估计方法,用于改进神经成像分析.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 生物物理学的生物物理.
背景情况:
- 扩散核磁共振和扩散放松计对于非侵入性检测组织微观结构至关重要.
- 精确估计微观结构参数需要复杂的建模和强大的装配技术.
- 现有的工具可能缺乏灵活性或全面的不确定性量化,以进行先进的分析.
研究的目的:
- 介绍Microstructure.jl,一个用于概率估计组织微结构参数的Julia包.
- 为定义分区模型和实施先进估计方法提供灵活的框架.
- 展示该包在优化采集协议和分析体内/体外MRI数据方面的实用性.
主要方法:
- 在Julia开发了Microstructure.jl,提供了一个灵活和可扩展的框架.
- 实施了已建立的模型,如球形平均技术 (SMT),标准模型成像 (SMI) 和索马和神经密度成像 (SANDI).
- 综合概率估计方法包括马尔科夫链蒙特卡罗 (MCMC) 和基于神经网络的蒙特卡罗脱落.
主要成果:
- 展示了Microstructure.jl在使用合成数据优化采集协议方面的能力.
- 评估了轴心直径指数估计的安装性能.
- 成功地应用了该包来分析体内人类连接体项目数据 (SMT) 和高梯度扫描仪数据 (SANDI),以及体外Prisma扫描仪数据 (免费用水的SMI).
结论:
- 微结构.jl为微结构参数估计和不确定性量化提供了一个强大,统一和灵活的平台.
- 该包支持各种MRI采集类型和扫描仪功能,适用于体内和体外数据.
- 它通过使复杂组织微观结构的强大分析,促进了先进的神经成像研究.
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