BACKGROUND Spatially normalizing brain MRI data to a template is commonly performed to facilitate comparisons between individuals or groups. However, the presence of multiple sclerosis (MS) lesions and other MS-related brain pathologies may compromise the performance of automated spatial normalization procedures. We therefore aimed to systematically compare five commonly used spatial normalization methods for brain MRI - including linear (affine), and nonlinear MRIStudio (LDDMM), FSL (FNIRT), ANTs (SyN), and SPM (CAT12) algorithms - to evaluate their performance in the presence of MS-related pathologies. METHODS 3 Tesla MRI images (T1-weighted and T2-FLAIR) were obtained for 20 participants with MS from an ongoing cohort study (used to assess a real dataset) and 1 healthy control participant (used to create a simulated lesion dataset). Both raw and lesion-filled versions of each participant's T1-weighted brain images were warped to the Montreal Neurological Institute (MNI) template using all five normalization approaches for the real dataset, and the same procedure was then repeated using the simulated lesion dataset (i.e., total of 400 spatial normalizations). As an additional quality-assurance check, the resulting deformations were also applied to the corresponding lesion masks to evaluate how each processing pipeline handled focal white matter lesions. For each normalization approach, inter-subject variability (across normalized T1-weighted images) was quantified using both mutual information (MI) and coefficient of variation (COV), and the corresponding normalized lesion volumes were evaluated using paired-sample t-tests. RESULTS All four nonlinear warping methods outperformed conventional linear normalization, with SPM (CAT12) yielding the highest MI values, lowest COV values, and proportionately-scaled lesion volumes. Although lesion-filling improved spatial normalization accuracy for each of the methods tested, these effects were small compared to differences between normalization algorithms. CONCLUSIONS SPM (CAT12) warping, ideally combined with lesion-filling, is recommended for use in future MS brain imaging studies requiring spatial normalization.

中文翻译：

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多发性硬化症大脑MRI数据的空间归一化取决于分析方法和软件包。

背景技术通常执行将脑MRI数据空间标准化为模板以促进个人或群体之间的比较。但是，多发性硬化（MS）病变和其他与MS相关的脑部病变的存在可能会损害自动空间标准化程序的性能。因此，我们旨在系统比较五个常用的大脑MRI空间归一化方法，包括线性（仿射）和非线性MRIStudio（LDDMM），FSL（FNIRT），ANT（SyN）和SPM（CAT12）算法，以评估其性能与MS相关的病理情况。方法从正在进行的队列研究（用于评估真实数据集）和1名健康对照参与者（用于创建模拟病变数据集）中，为20名患有MS的参与者获得3张Tesla MRI图像（T1加权和T2-FLAIR）。使用针对真实数据集的所有五种归一化方法，将每个参与者的T1加权脑图像的原始版本和病变填充版本都扭曲到蒙特利尔神经学研究所（MNI）模板，然后使用模拟的病变数据集重复相同的过程（即，总共400个空间归一化）。作为附加的质量保证检查，还将所得的变形应用于相应的病变蒙版，以评估每个加工管道如何处理局灶性白质病变。对于每种归一化方法，使用互信息（MI）和变异系数（COV）对受试者间的变异性（跨归一化的T1加权图像）进行量化，并使用成对样本t检验评估相应的归一化病变体积。结果所有四种非线性翘曲方法均优于常规线性归一化，其中SPM（CAT12）产生最高MI值，最低​​COV值和成比例缩放的病变体积。尽管针对每种测试方法，病灶填充提高了空间标准化的准确性，但与标准化算法之间的差异相比，这些影响很小。结论建议将SPM（CAT12）翘曲与病灶填充完美结合，建议用于未来需要空间归一化的MS脑成像研究。与归一化算法之间的差异相比，这些影响很小。结论建议将SPM（CAT12）翘曲与病灶填充完美结合，建议用于未来需要空间归一化的MS脑成像研究。与归一化算法之间的差异相比，这些影响很小。结论建议将SPM（CAT12）翘曲与病灶填充完美结合，建议用于未来需要空间归一化的MS脑成像研究。