Evaluation of MRI and cannabinoid type 1 receptor PET templates constructed using DARTEL for spatial normalization of rat brains.
Evaluation of MRI and cannabinoid type 1 receptor PET templates constructed using DARTEL for spatial normalization of rat brains.
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使用 DARTEL 构建的 MRI 和大麻素 1 型受体 PET 模板对大鼠大脑空间标准化的评估
DOI：
10.1118/1.4934825
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发表时间：
2015
期刊：
影响因子：
3.8
通讯作者：
Maus S
中科院分区：
文献类型：
--
作者：
Kronfeld A;Buchholz H-G;Maus S
目的：在磁共振成像（MRI）或正电子发射断层扫描（PET）研究中，图像配准是分析大脑区域的先决条件。指数李代数微分解剖配准（DARTEL）是一种用于图像配准和图像模板构建的非线性微分配准算法。这项小动物研究的目的是：(1)评估MRI并计算使用DARTEL构建的几种大麻素1型（CB1）受体PET模板；(2)参考分析和迭代PET重建算法，分析MR和PET图像与其DARTEL模板的图像配准精度。方法采用MRI和[18F]MK‐9470 PET对5只雄性Sprague Dawley大鼠进行CB1受体表征，构建模板。PET图像使用滤波后投影、二维有序子集期望最大化和三维后置最大化算法重建。在每张MR图像上定义地标，并在不同的设置下构建模板，即基于不同的组织类图像[灰质（GM），白质（WM）和GM + WM]和正则化形式（“线性弹性能”，“膜能”和“弯曲能”）。通过地标坐标之间的距离来评价MRI和PET模板的配准精度。结果基于灰、白质图像和线性弹性能的正则化构建了最佳的MRI模板。在这种情况下，大多数地标坐标之间的距离都小于1mm。因此，基于MRI的空间归一化是最准确的，但基于PET的空间归一化的结果是相当可比的。结论基于DARTEL的图像配准为小动物脑数据分析提供了标准化、自动化的框架。结果表明，该方法具有较高的信度和效度。使用DARTEL模板和非线性配准算法可以对MRI/PET或仅PET研究进行精确的空间归一化。
PurposeImage registration is one prerequisite for the analysis of brain regions in magnetic‐resonance‐imaging (MRI) or positron‐emission‐tomography (PET) studies. Diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) is a nonlinear, diffeomorphic algorithm for image registration and construction of image templates. The goal of this small animal study was (1) the evaluation of a MRI and calculation of several cannabinoid type 1 (CB1) receptor PET templates constructed using DARTEL and (2) the analysis of the image registration accuracy of MR and PET images to their DARTEL templates with reference to analytical and iterative PET reconstruction algorithms.MethodsFive male Sprague Dawley rats were investigated for template construction using MRI and [18F]MK‐9470 PET for CB1 receptor representation. PET images were reconstructed using the algorithms filtered back‐projection, ordered subset expectation maximization in 2D, and maximuma posterioriin 3D. Landmarks were defined on each MR image, and templates were constructed under different settings, i.e., based on different tissue class images [gray matter (GM), white matter (WM), and GM + WM] and regularization forms (“linear elastic energy,” “membrane energy,” and “bending energy”). Registration accuracy for MRI and PET templates was evaluated by means of the distance between landmark coordinates.ResultsThe best MRI template was constructed based on gray and white matter images and the regularization form linear elastic energy. In this case, most distances between landmark coordinates were <1 mm. Accordingly, MRI‐based spatial normalization was most accurate, but results of the PET‐based spatial normalization were quite comparable.ConclusionsImage registration using DARTEL provides a standardized and automatic framework for small animal brain data analysis. The authors were able to show that this method works with high reliability and validity. Using DARTEL templates together with nonlinear registration algorithms allows for accurate spatial normalization of combined MRI/PET or PET‐only studies.
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影响因子：
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作者：
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