UR370 QuaPA, INRA, F-63122 Saint-Genès-Champanelle, France.
Magn Reson Imaging. 2011 Nov;29(9):1304-16. doi: 10.1016/j.mri.2011.07.014. Epub 2011 Sep 9.
Manganese (Mn)-enhanced magnetic resonance imaging (MEMRI) is an emerging technique for visualizing neuronal pathways and mapping brain activity modulation in animal models. Spatial and intensity normalizations of MEMRI images acquired from different subjects are crucial steps as they can influence the results of groupwise analysis. However, no commonly accepted procedure has yet emerged. Here, a normalization method is proposed that performs both spatial and intensity normalizations in a single iterative process without the arbitrary choice of a reference image. Spatial and intensity normalizations benefit from this iterative process. On one hand, spatial normalization increases the accuracy of region of interest (ROI) positioning for intensity normalization. On the other hand, improving the intensity normalization of the different MEMRI images leads to a better-averaged target on which the images are spatially registered. After automatic fast brain segmentation and optimization of the normalization process, this algorithm revealed the presence of Mn up to the posterior entorhinal cortex in a tract-tracing experiment on rat olfactory pathways. Quantitative comparison of registration algorithms showed that a rigid model with anisotropic scaling is the best deformation model for intersubject registration of three-dimensional MEMRI images. Furthermore, intensity normalization errors may occur if the ROI chosen for intensity normalization intersects regions where Mn concentration differs between experimental groups. Our study suggests that cross-comparing Mn-injected animals against a Mn-free group may provide a control to avoid bias introduced by intensity normalization quality. It is essential to optimize spatial and intensity normalization as the detectability of local between-group variations in Mn concentration is directly tied to normalization quality.
锰增强磁共振成像（MEMRI）是一种新兴的技术，用于可视化动物模型中的神经元通路和映射大脑活动调节。从不同对象获取的 MEMRI 图像的空间和强度归一化是关键步骤，因为它们会影响组分析的结果。然而，目前还没有出现普遍接受的方法。在这里，提出了一种归一化方法，该方法在单个迭代过程中同时进行空间和强度归一化，而无需任意选择参考图像。空间和强度归一化都受益于这个迭代过程。一方面，空间归一化提高了 ROI 定位的准确性，以进行强度归一化。另一方面，改善不同 MEMRI 图像的强度归一化可以更好地对目标进行平均，然后对图像进行空间配准。在自动快速大脑分割和归一化过程优化后，该算法在大鼠嗅觉通路示踪实验中揭示了 Mn 一直延伸到后内嗅皮层的存在。注册算法的定量比较表明，对于三维 MEMRI 图像的受试者间配准，具有各向异性缩放的刚性模型是最佳的变形模型。此外，如果用于强度归一化的 ROI 与 Mn 浓度在实验组之间不同的区域相交，则可能会出现强度归一化错误。我们的研究表明，对 Mn 注射动物与无 Mn 组进行交叉比较可以提供一种控制方法，以避免因强度归一化质量引起的偏差。优化空间和强度归一化非常重要，因为 Mn 浓度的局部组间变化的检测能力直接与归一化质量有关。
