Salas-Gonzalez D, Górriz Juan M, Ramírez Javier, Illán Ignacio A, Padilla Pablo, Martínez-Murcia Francisco J, Lang Elmar W
Computational Intelligence and Machine Learning Group, University of Regensburg, 93040, Regensburg, Germany.
Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain.
Neuroinformatics. 2015 Oct;13(4):391-402. doi: 10.1007/s12021-015-9262-9.
Spatial affine registration of brain images to a common template is usually performed as a preprocessing step in intersubject and intrasubject comparison studies, computer-aided diagnosis, region of interest selection and brain segmentation in tomography. Nevertheless, it is not straightforward to build a template of [123I]FP-CIT SPECT brain images because they exhibit very low intensity values outside the striatum. In this work, we present a procedure to automatically build a [123I]FP-CIT SPECT template in the standard Montreal Neurological Institute (MNI) space. The proposed methodology consists of a head voxel selection using the Otsu's method, followed by a posterization of the source images to three different levels: background, head, and striatum. Analogously, we also design a posterized version of a brain image in the MNI space; subsequently, we perform a spatial affine registration of the posterized source images to this image. The intensity of the transformed images is normalized linearly, assuming that the histogram of the intensity values follows an alpha-stable distribution. Lastly, we build the [123I]FP-CIT SPECT template by means of the transformed and normalized images. The proposed methodology is a fully automatic procedure that has been shown to work accurately even when a high-resolution magnetic resonance image for each subject is not available.
在受试者间和受试者内比较研究、计算机辅助诊断、断层扫描中的感兴趣区域选择和脑部分割等研究中，通常会将脑图像与通用模板进行空间仿射配准作为预处理步骤。然而，构建[123I]FP-CIT单光子发射计算机断层扫描（SPECT）脑图像模板并非易事，因为纹状体之外的区域强度值非常低。在这项工作中，我们提出了一种在标准蒙特利尔神经病学研究所（MNI）空间中自动构建[123I]FP-CIT SPECT模板的方法。所提出的方法包括使用大津法进行头部体素选择，然后将源图像后处理为三个不同级别：背景、头部和纹状体。类似地，我们还设计了MNI空间中脑图像的后处理版本；随后，我们将后处理后的源图像与该图像进行空间仿射配准。假设强度值的直方图遵循α稳定分布，对变换后的图像强度进行线性归一化。最后，我们通过变换和归一化后的图像构建[123I]FP-CIT SPECT模板。所提出的方法是一个完全自动化的过程，即使没有每个受试者的高分辨率磁共振图像，也已证明该方法能准确工作。
