Deformation field correction for spatial normalization of PET images.
Deformation field correction for spatial normalization of PET images.
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DOI：
10.1016/j.neuroimage.2015.06.063
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发表时间：
2015-10-01
期刊：
影响因子：
5.7
通讯作者：
Prince JL
中科院分区：
文献类型：
--
作者：
Bilgel M;Carass A;Resnick SM;Wong DF;Prince JL
正电子发射断层扫描（PET）图像的空间归一化对于人群研究至关重要，但PET到PET配准的当前技术水平仅限于应用为结构图像开发的传统可变形配准方法。提出了一种用于PET图像的空间归一化的方法，该方法改进了它们在现有技术水平上的解剖对准。该方法通过使用从具有PET和结构图像的训练数据中学习的模型来校正可变形配准结果来工作。特别地，将训练数据的结构配准视为地面实况，通过在给定基于群体的PET模板中的PET强度和体素位置的情况下在每个体素处使用广义岭回归来学习校正因子。然后可以使用训练的模型来获得PET图像到PET模板的更准确的配准，而不使用结构图像。对79名受试者的交叉验证评价表明，与变形PET到PET配准相比，所提出的方法产生了更准确的PET图像对齐，如1）变形图像的视觉检查，2）变形场中的较小误差，以及3）变形解剖标签与地面实况分割的较大重叠所揭示的。
Spatial normalization of positron emission tomography (PET) images is essential for population studies, yet the current state of the art in PET-to-PET registration is limited to the application of conventional deformable registration methods that were developed for structural images. A method is presented for the spatial normalization of PET images that improves their anatomical alignment over the state of the art. The approach works by correcting the deformable registration result using a model that is learned from training data having both PET and structural images. In particular, viewing the structural registration of training data as ground truth, correction factors are learned by using a generalized ridge regression at each voxel given the PET intensities and voxel locations in a population-based PET template. The trained model can then be used to obtain more accurate registration of PET images to the PET template without the use of a structural image. A cross validation evaluation on 79 subjects shows that the proposed method yields more accurate alignment of the PET images compared to deformable PET-to-PET registration as revealed by 1) a visual examination of the deformed images, 2) a smaller error in the deformation fields, and 3) a greater overlap of the deformed anatomical labels with ground truth segmentations.