对比主流工具在脑影像配准中的数值稳定性，发现SPM最可靠。
Numerical Uncertainty in Linear Registration: An Experimental Study
- 用蒙特卡洛算术模拟评估三种软件的配准数值误差
- SPM稳定性最高，ANTS易因微小扰动导致失败
- 健康与帕金森患者数据无差异，结果可推广至临床
线性配准是MRI预处理的关键步骤，但其数值不确定性研究不足。我们采用蒙特卡洛算术（MCA）模拟，评估了SPM、FSL和ANTs三大软件包中常用线性配准工具，在多种图像相似性度量、两个脑模板及健康对照组（n=50）和帕金森病患者组（n=50）上的表现。结果显示，不同工具和相似性度量显著影响数值稳定性。在默认参数下，SPM表现最稳定，FSL与ANTs变异性较大且相近，其中ANTs对数值扰动尤为敏感，偶发配准失败。健康与帕金森患者间未见显著差异，表明基于健康人群的稳定性分析可推广至临床群体。此外，数值不确定性度量可用于自动质量控制。本研究实验刻画了线性配准的数值稳定性，为未来不确定性分析提供基础。
原文摘要 · Abstract (English)
While linear registration is a critical step in MRI preprocessing pipelines, its numerical uncertainty is understudied. Using Monte-Carlo Arithmetic (MCA) simulations, we assessed the most commonly used linear registration tools within major software packages (SPM, FSL, and ANTs) across multiple image similarity measures, two brain templates, and both healthy control (HC, n=50) and Parkinson's Disease (PD, n=50) cohorts. Our findings highlight the influence of linear registration tools and similarity measures on numerical stability. Among the evaluated tools and with default similarity measures, SPM exhibited the highest stability. FSL and ANTs showed greater and similar ranges of variability, with ANTs demonstrating particular sensitivity to numerical perturbations that occasionally led to registration failure. Furthermore, no significant differences were observed between healthy and PD cohorts, suggesting that numerical stability analyses obtained with healthy subjects may generalise to clinical populations. Finally, we also demonstrated how numerical uncertainty measures may support automated quality control (QC) of linear registration results. Overall, our experimental results characterize the numerical stability of linear registration experimentally and can serve as a basis for future uncertainty analyses.
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