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Applying a 11Craclopride template to automated binding potential estimation in HRRT brain PET

机译: 11 C雷氯必利模板应用于HRRT脑PET的自动结合电位估计

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Performing kinetic analysis of [11C]raclopride PET data typically involves user intervention to identify key volumes of interest, such as the cerebellum for the reference region and the caudate and putamen for regions where the binding potential (BP) needs to be estimated. In many PET centres, this process is neither automated nor standardized, possibly producing discrepancies between centres. Conventionally, MR anatomical images are used to identify the key volumes of interest, but this is difficult to automate robustly, and user intervention can sometimes be required. This work considers instead the use of an anatomically labeled [11C]raclopride template, formed from multiple subjects, which has the key advantages of low noise, good resolution and having a highly similar spatiotemporal intensity distribution to any given single subject raclopride scan. This makes the template an excellent target for automated image registration and segmentation. We present a methodology which works on post-reconstruction images, demonstrating an automated and consistent way of identifying key regions of interest (ROIs) and determining binding potential without any MR image or user intervention. The performance of the methodology is evaluated using simulated and real [11C]raclopride data. The simplified reference tissue model with the basis function method (SRTM-BFM) was used for the kinetic modeling.
机译:对[ 11 C] raclopride PET数据进行动力学分析通常需要用户干预才能确定感兴趣的关键体积,例如参考区域的小脑以及结合潜力(BP)的区域的尾状和壳状核)需要估算。在许多PET中心中,此过程既没有自动化也没有标准化,可能会导致中心之间的差异。常规上,MR解剖图像用于识别感兴趣的关键体积,但是很难稳健地实现自动化,有时可能需要用户干预。这项工作反而考虑使用由多个对象组成的解剖学标记的[ 11 C] raclopride模板,该模板具有以下优点:低噪声,良好的分辨率以及与任何时空强度分布高度相似的给定单一主题雷克必利扫描。这使模板成为自动图像配准和分割的理想目标。我们提供了一种在重建后图像上工作的方法,展示了一种自动且一致的方法来识别关键关注区域(ROI)并确定结合潜力,而无需任何MR图像或用户干预。该方法的性能是使用模拟的和真实的[ 11 C] raclopride数据进行评估的。使用具有基本功能方法的简化参考组织模型(SRTM-BFM)进行动力学建模。

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