Center for Integrated Molecular Brain Imaging (Cimbi) and Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark.
Center for Integrated Molecular Brain Imaging (Cimbi) and Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
J Neurosci Methods. 2018 Jan 15;294:51-58. doi: 10.1016/j.jneumeth.2017.11.008. Epub 2017 Nov 13.
The increasing use of the pig as a research model in neuroimaging requires standardized processing tools. For example, extraction of regional dynamic time series from brain PET images requires parcellation procedures that benefit from being automated.
Manual inter-modality spatial normalization to a MRI atlas is operator-dependent, time-consuming, and can be inaccurate with lack of cortical radiotracer binding or skull uptake.
A parcellated PET template that allows for automatic spatial normalization to PET images of any radiotracer.
MRI and [C]Cimbi-36 PET scans obtained in sixteen pigs made the basis for the atlas. The high resolution MRI scans allowed for creation of an accurately averaged MRI template. By aligning the within-subject PET scans to their MRI counterparts, an averaged PET template was created in the same space. We developed an automatic procedure for spatial normalization of the averaged PET template to new PET images and hereby facilitated transfer of the atlas regional parcellation. Evaluation of the automatic spatial normalization procedure found the median voxel displacement to be 0.22±0.08mm using the MRI template with individual MRI images and 0.92±0.26mm using the PET template with individual [C]Cimbi-36 PET images. We tested the automatic procedure by assessing eleven PET radiotracers with different kinetics and spatial distributions by using perfusion-weighted images of early PET time frames.
We here present an automatic procedure for accurate and reproducible spatial normalization and parcellation of pig PET images of any radiotracer with reasonable blood-brain barrier penetration.
猪作为神经影像学研究模型的应用越来越广泛，这就需要标准化的处理工具。例如，从脑 PET 图像中提取区域动态时间序列需要进行分割处理，而这些分割处理最好是自动化的。
手动进行跨模态空间标准化到 MRI 图谱是依赖于操作者的，既费时又费力，并且在皮质放射性示踪剂结合或颅骨摄取不足的情况下可能不准确。
一个分割的 PET 模板，允许对任何放射性示踪剂的 PET 图像进行自动空间标准化。
十六头猪的 MRI 和 [C]Cimbi-36 PET 扫描为图谱提供了基础。高分辨率的 MRI 扫描允许创建一个精确的平均 MRI 模板。通过将个体 PET 扫描与 MRI 对应物对齐，在相同的空间中创建了一个平均 PET 模板。我们开发了一种自动程序，将平均 PET 模板自动空间标准化到新的 PET 图像上，从而方便了图谱的区域分割。评估自动空间标准化程序发现，使用个体 MRI 图像的 MRI 模板时，中位数的体素位移为 0.22±0.08mm，使用个体 [C]Cimbi-36 PET 图像的 PET 模板时，中位数的体素位移为 0.92±0.26mm。我们通过使用早期 PET 时间帧的灌注加权图像评估了 11 种具有不同动力学和空间分布的 PET 放射性示踪剂，对自动程序进行了测试。
我们在此提出了一种准确且可重复的空间标准化和分割猪任何放射性示踪剂的 PET 图像的自动程序，该程序具有合理的血脑屏障穿透性。
