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Improving alignment in Tract-based spatial statistics: Evaluation and optimization of image registration

机译：改进基于Tract的空间统计中的对齐方式：图像配准的评估和优化

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Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS establishes spatial correspondence using a combination of nonlinear registration and a "skeleton projection" that may break topological consistency of the transformed brain images. We therefore investigated feasibility of replacing the two-stage registration-projection procedure in TBSS with a single, regularized, high-dimensional registration.To optimize registration parameters and to evaluate registration performance in diffusion MRI, we designed an evaluation framework that uses native space probabilistic tractography for 23 white matter tracts, and quantifies tract similarity across subjects in standard space. We optimized parameters for two registration algorithms on two diffusion datasets of different quality. We investigated reproducibility of the evaluation framework, and of the optimized registration algorithms. Next, we compared registration performance of the regularized registration methods and TBSS. Finally, feasibility and effect of incorporating the improved registration in TBSS were evaluated in an example study.The evaluation framework was highly reproducible for both algorithms (R2 0.993; 0.931). The optimal registration parameters depended on the quality of the dataset in a graded and predictable manner. At optimal parameters, both algorithms outperformed the registration of TBSS, showing feasibility of adopting such approaches in TBSS. This was further confirmed in the example experiment.
机译：神经影像学研究中的解剖学对准非常重要，以致于在改进用于建立空间对应关系的配准上付出了巨大的努力。基于行迹的空间统计（TBSS）是一种比较对象之间扩散特征的流行方法。 TBSS使用非线性配准和“骨架投影”的组合来建立空间对应关系，这可能会破坏转换后的大脑图像的拓扑一致性。因此，我们研究了用单一的正则化高维配准替换TBSS中的两阶段配准投影程序的可行性。为了优化配准参数并评估扩散MRI中的配准性能，我们设计了一个使用本机空间概率的评估框架进行了23个白质束的束线照相术，并量化了标准空间中各个对象的束带相似度。我们针对两个不同质量的扩散数据集的两种配准算法优化了参数。我们研究了评估框架和优化注册算法的可重复性。接下来，我们比较了常规注册方法和TBSS的注册性能。最后，在一个实例研究中评估了将改进的配准纳入TBSS的可行性和效果。两种算法的评估框架均具有高度可重复性（R2 0.993; 0.931）。最佳配准参数以分级且可预测的方式取决于数据集的质量。在最佳参数下，两种算法均优于TBSS的注册，这表明在TBSS中采用此类方法的可行性。实例实验进一步证实了这一点。

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