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基于voxel的病变症状映射的空间规范化:注册方法的影响

Daniel Jühling^1,2, Deepthi Rajashekar³, Bastian Cheng⁴

¹Institute of Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

Frontiers in neuroscience

|February 1, 2024

概括

基于声素的病变症状映射 (VLSM) 的非线性注册方法可能会引入图像扭曲,质疑它们与用于预测中风结果的更简单的亲属注册相比的好处. 仔细考虑空间规范化对于准确的VLSM分析至关重要.

科学领域:

神经成像是一种神经成像.
神经学神经学
医学图像分析医学图像分析

背景情况:

基于Voxel的病变症状映射 (VLSM) 将病变位置与临床结果联系起来.
空间规范化是VLSM的关键预处理步骤.
对VLSM的最佳注册方法仍在争论中,这影响了统计分析.

研究的目的:

评估VLSM的四种常见的空间规范化注册方法.
评估这些注册方法对VLSM统计的下游影响.
为了确定最适合的注册方法中风成像数据.

主要方法:

对比的亲属 (AR),非线性 (NLR),成本函数掩盖 (CFM) 和反方形 (ENR) 登记.
利用了来自122名急性缺血性中风患者的FLAIR MRI数据.
通过使用单变量和多变量统计数据,对30天修改的兰金尺度 (mRS) 进行了损伤-声素关系的评估.

主要成果:

对于mRS结果的关键大脑区域在所有注册方法中都是一致的.
非线性方法 (NLR,CFM,ENR) 引入了图像扭曲.
非线性注册导致局部结构和邻近的大脑区域的对齐不足.

结论:

对于中风研究来说,非线性注册变体对VLSM空间规范化的好处似乎值得怀疑.
非线性注册造成的图像扭曲会在VLSM分析中带来不确定性.
在类似的VLSM研究中,亲缘注册可能是空间规范化的更可靠选择.

关键词:

这是VLSMSM.图像注册图像注册图像注册神经成像是一种神经成像.空间规范化的空间规范化一次性中风中风中风中风中风

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