Optimization of PET-MR registrations for nonhuman primates using mutual information measures: a Multi-Transform Method (MTM).
Optimization of PET-MR registrations for nonhuman primates using mutual information measures: a Multi-Transform Method (MTM).
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DOI：
10.1016/j.neuroimage.2012.08.051
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发表时间：
2013-01-01
期刊：
影响因子：
5.7
通讯作者：
Carson, Richard E.
中科院分区：
文献类型：
--
作者：
Sandiego, Christine M.;Weinzimmer, David;Carson, Richard E.
PET脑运动分析的一个重要步骤是将功能数据与解剖MR图像配准。通常，在非人类灵长类神经感受器研究中，PET-MR注册使用在注射后早期(例如，0-10分钟)获得的PET图像，以与受试者的MR图像非常相似。然而，由于各种放射性示踪剂研究和条件(例如，阻断研究)的动力学和分布的差异，这些注册中有相当一部分(~25%)失败。为了提高PET和MR图像的配准成功率，提出了多重变换方法(MTM)。对两种算法MTM-I和MTM-II进行了评估。该方法包括通过配准不同时间间隔的PET图像来创建多个变换，从动态研究到单个参考(即，MR图像)(MTM-I)或到多个参考图像(即，预先配准到MR的MR和PET图像)(MTM-II)。利用归一化互信息计算变换后的图像与参考图像(S)之间的相似度，选择最优的变换。这一最终转换用于将动态数据集映射到动物的解剖MR空间，这是动力学分析所需的。使用视觉评分评估从MTM-I和MTM-II转换而来的所选对象，以评估重新切除的PET与参考之间的空间排列质量。使用来自3种不同扫描仪的11种不同示踪剂的120个PET数据集来评估MTM算法。研究对象为狒狒和恒河猴，研究对象为HR+、HRRT和Focus-220。根据视觉评分，0-10分钟法、MTM-I法和MTM-II法的成功率分别从77.5%、85.8%增加到96.7%。多变换方法被证明是一种稳健的技术，用于广泛的PET研究的PET-MR配准。
An important step in PET brain kinetic analysis is the registration of functional data to an anatomical MR image. Typically, PET-MR registrations in nonhuman primate neuroreceptor studies used PET images acquired early post-injection, (e.g., 0–10 min) to closely resemble the subject’s MR image. However, a substantial fraction of these registrations (~25%) fail due to the differences in kinetics and distribution for various radiotracer studies and conditions (e.g., blocking studies). The Multi-Transform Method (MTM) was developed to improve the success of registrations between PET and MR images. Two algorithms were evaluated, MTM-I and MTM-II. The approach involves creating multiple transformations by registering PET images of different time intervals, from a dynamic study, to a single reference (i.e., MR image) (MTM-I) or to multiple reference images (i.e., MR and PET images pre-registered to the MR) (MTM-II). Normalized mutual information was used to compute similarity between the transformed PET images and the reference image(s) to choose the optimal transformation. This final transformation is used to map the dynamic dataset into the animal’s anatomical MR space, required for kinetic analysis. The chosen transformed from MTM-I and MTM-II were evaluated using visual rating scores to assess the quality of spatial alignment between the resliced PET and reference. One hundred twenty PET datasets involving eleven different tracers from 3 different scanners were used to evaluate the MTM algorithms. Studies were performed with baboons and rhesus monkeys on the HR+, HRRT, and Focus-220. Successful transformations increased from 77.5%, 85.8%, to 96.7% using the 0–10 min method, MTM-I, and MTM-II, respectively, based on visual rating scores. The Multi-Transform Methods proved to be a robust technique for PET-MR registrations for a wide range of PET studies.
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