PCA MNI 2009b NLIN template
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This is the first principal component combining T1-, T2- and proton-density (PD)-weighted templates of the MNI 2009b high resolution nonlinear asymmetric template series.<br>This single template retains ≥ 87.34% of variance in T1, T2 and PD templates combined within the brain segment of the templates.<br>This additional PCA template may be used for multispectral normalizations (e.g. as implemented in Lead-DBS software1).<br><b>Example case:</b> You did acquire T1-, T2-, PD- acquisitions from a subject but also acquired an inversion recovery (IR) sequence and you wish to combine all anatomical information to warp into standard space.<br>In this case, you could set up a combined nonlinear warp matching:<br>T1 acquisition -> T1 templateT2 acquisition -> T2 template<br>PD acquisition -> PD template<br>IR acquisition -> PCA template* (since no IR template available).<br><br>* using e.g. normalized mutual information as a convergence metric.<br>There is no empirical data to support this makes sense, but intuitively, the PCA template should be the most "universal" and contain information from all three templates available from the MNI.<br><br>Code to (re-) generate this template is available within Lead-DBS ('ea_pca_templates.m').
本模板为结合MNI 2009b高分辨率非线性不对称模板系列中T1加权、T2加权及质子密度（PD）加权模板所构建的第一主成分。<br>该单一模板在模板的脑区片段内，保留了T1、T2与PD模板合并后总方差的87.34%及以上。<br>该额外的主成分分析（PCA）模板可用于多光谱标准化处理（例如在Lead-DBS软件中实现的多光谱标准化流程）¹。<br><b>示例场景：</b>若你已从某受试者处获取了T1、T2及PD加权成像数据，同时还采集了反转恢复（IR）序列数据，且希望整合所有解剖信息以将图像配准至标准空间。<br>此时你可搭建如下组合式非线性配准匹配流程：<br>T1成像数据 → T1模板<br>T2成像数据 → T2模板<br>PD成像数据 → PD模板<br>IR成像数据 → PCA模板*（因无对应IR模板可用）。<br><br>* 例如可采用归一化互信息作为收敛度量指标。<br>目前尚无实证数据支持该方案的合理性，但从直觉上而言，PCA模板应当是最具“普适性”的模板，且包含了MNI提供的全部三类模板的信息。<br><br>用于（重新）生成该模板的代码可在Lead-DBS软件的`ea_pca_templates.m`文件中获取。
