Spatial and intensity normalization are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging modalities. Nuclear imaging modalities such as PET-FDG or FP-CIT SPECT, a common modality used in Parkinson's Disease diagnosis, are especially dependent on intensity normalization. However, these steps are computationally expensive and furthermore, they may introduce deformations in the images, altering the information contained in them. Convolutional Neural Networks (CNNs), for their part, introduce position invariance to pattern recognition, and have been proven to classify objects regardless of their orientation, size, angle, etc. Therefore, a question arises: how well can CNNs account for spatial and intensity differences when analysing nuclear brain imaging? Are spatial and intensity normalization still needed? To answer this question, we have trained four different CNN models based on well-established architectures, using or not different spatial and intensity normalization preprocessing. The results show that a sufficiently complex model such as our three-dimensional version of the ALEXNET can effectively account for spatial differences, achieving a diagnosis accuracy of 94.1% with an area under the ROC curve of 0.984. The visualization of the differences via saliency maps shows that these models are correctly finding patterns that match those found in the literature, without the need of applying any complex spatial normalization procedure. However, the intensity normalization -- and its type -- is revealed as very influential in the results and accuracy of the trained model, and therefore must be well accounted.

翻译：空间和强度归一化如今是神经影像分析的前提条件。受体素级及其他单变量比较方法的影响——在这些方法中，上述校正是关键——它们通常被应用于各类分析和成像模态。核医学成像模态如PET-FDG或FP-CIT SPECT（帕金森病诊断中常用模态）尤其依赖强度归一化。然而，这些步骤计算成本高昂，且可能在图像中引入变形，改变其中包含的信息。卷积神经网络则具有位置不变性的模式识别能力，已被证明能够识别不同朝向、尺寸、角度等条件下的目标。因此产生一个疑问：在分析脑核医学影像时，CNN在多大程度上能处理空间和强度差异？是否仍需进行空间和强度归一化？为回答此问题，我们基于成熟架构训练了四种不同的CNN模型，分别采用或不采用不同的空间和强度归一化预处理。结果表明，足够复杂的模型（如我们提出的三维ALEXNET版本）能有效处理空间差异，在ROC曲线下面积达0.984的情况下实现94.1%的诊断准确率。通过显著性图可视化差异显示，这些模型能准确识别与文献报道相符的特征模式，无需应用任何复杂空间归一化流程。但强度归一化及其类型对训练模型的性能和准确率具有显著影响，因此必须妥善处理。