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隐藏在眼前的血管:用深度学习在解剖学MRI图像中量化大脑血管形态

Asa Gilmore^1,2, Anita Esi Eshun³, Yue Wu^4,5

¹Department of Psychology, University of Washington, Guthrie 119A, Seattle, 98195, WA, USA.

bioRxiv : the preprint server for biology

|July 14, 2025

概括

一个新的神经网络模型,anat2vessels,从标准MRI扫描中量化大脑血管形态. 这种方法揭示了对大脑衰老,血压和认知能力的洞察力,为大脑健康研究提供了宝贵的工具.

科学领域:

神经成像是一种神经成像.
医学图像分析医学图像分析
计算神经科学是一种神经科学.

背景情况:

使用磁共振 (MRI) 影像对大脑血管系统进行非侵入性评估对于了解大脑健康和衰老至关重要.
飞行时间MR血管造影 (TOF-MRA) 是可视化血管形态的常用技术,但比标准T1或T2加权成像 (T1w/T2w) 耗时且使用频率较低.
需要使用方法来从广泛可用的T1w/T2wMR图像直接量化大脑血管形态.

研究的目的:

开发和验证一种神经网络模型 (anat2vessels),能够从标准T1w/T2wMRI图像中对大脑血管进行细分.
与地面真相数据相比,评估模型细分的准确性.
调查来自T1w/T2w图像的血管形态特征的实用性,以了解大脑衰老,血压和认知功能.

主要方法:

在对MR/MRA图像的数据集上训练一个神经网络 (anat2vessels),以了解标准对比度和血管形态之间的关系.
评估模型性能,使用对基准真相细分的交叉验证.
从模型衍生细分和基本事实中提取和关联形态特征.
在一个单独的数据集中,评估 anat2vessels 衍生的血管形态特征与衰老,皮质厚度,血压和认知能力的关联.

主要成果:

anat2vessels模型实现了精确的船体细分,特别是在使用T2w图像时,从细分中提取的特征与地面真相相对应高达78%.
从使用anat2vessels的T1w/T2w图像中获得的血管形态特征,捕获了标准皮层厚度测量中不存在的关于衰老的信息.
血管形态特征与个人血压和认知能力的变化有关.

结论:

anat2vessels模型能够从标准T1w/T2wMR图像中准确量化大脑血管形态,克服TOF-MRA的局限性.
这种方法为大脑血管形态在衰老,血压调节和认知功能中的作用提供了新的见解.
anat2vessels的开源可用性促进了其在各种研究环境中的应用,以推进对大脑健康和疾病的理解.

关键词:

衰老的衰老衰老的衰老脑子脑子脑子脑子磁共振血管学磁共振血管学磁共振成像技术磁共振成像技术

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