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从T1权重的MRI中分离thalamic核:统一和对比最先进的方法

Brendan Williams^1,2, Dan Nguyen³, Julie P Vidal^4,5

¹Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Reading, United Kingdom.

Imaging neuroscience (Cambridge, Mass.)

|March 5, 2025

概括

准确地细分thalamic核对于理解大脑功能和疾病至关重要. 希普斯-托马斯方法最好的细分单个细胞核,并区分阿尔茨海默病与对照,有助于神经成像研究.

科学领域:

神经成像是一种神经成像.
神经解剖学是一个神经解剖学.
神经学神经学

背景情况:

乳头核对于认知和感觉运动功能至关重要.
神经疾病通常涉及 thalamus.
在神经成像中,准确地细分thalamic核是具有挑战性的,因为技术上的困难和缺乏标准化的命名法.

研究的目的:

为了对四种最先进的T1MRI胸膜细分方法进行基准测试.
为了将细分精度与莫雷尔立体学图谱进行比较.
评估方法在阿尔茨海默氏症进展中检测乳头膜退化的能力,并将患者组与健康对照区分开来.

主要方法:

利用了来自健康的年轻成年人 (人类结合体项目,n=100) 和老年人,轻度认知障碍和阿尔茨海默病患者 (阿尔茨海默病神经影像倡议,n=540) 的数据.
基于基准的FreeSurfer,HIPS-THOMAS,SCS-CNN和T1-THOMAS的细分方法. 这种方法是基于基准的FreeSurfer,HIPS-THOMAS,SCS-CNN和T1-THOMAS细分方法.
使用重叠和不相似度指标对比的细分与莫雷尔地图,以及基于thalamic体积的量化诊断歧视.

主要成果:

与莫雷尔地图集相比,HIPS-THOMAS提供了与莫雷尔地图集相比,最有效的单个乳头核细分.
希普斯-托马斯精确地区分了健康对照从轻度认知障碍和阿尔茨海默病患者使用单个核体积.
在使用整体 thalamus 卷时,SCS-CNN 在对健康对照进行分类时最准确.

结论:

这项研究首次在统一的框架下系统地比较了胸膜细分方法.
建议使用HIPS-THOMAS用于专注于特定乳头核的功能相关性的研究.
在疾病分类中,SCS-CNN可能适合进行全 thalamus 分析.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.免费冲浪者 (FreeSurfer) 是一个自由冲浪者.托马斯·托马斯 (Thomas Thomas) 是一个著名的作家.人类连接ome项目细分化细分化的细分化泰拉姆斯 (thalamus) 是一个神经系统.

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