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Magia: Robust Automated Image Processing and Kinetic Modeling Toolbox for PET Neuroinformatics
Frontiers in Neuroinformatics ( IF 3.5 ) Pub Date : 2020-02-04 , DOI: 10.3389/fninf.2020.00003
Tomi Karjalainen 1 , Jouni Tuisku 1 , Severi Santavirta 1 , Tatu Kantonen 1 , Marco Bucci 1 , Lauri Tuominen 2 , Jussi Hirvonen 1, 3 , Jarmo Hietala 1, 4 , Juha O Rinne 1, 5 , Lauri Nummenmaa 1, 6
Affiliation  

Processing of positron emission tomography (PET) data typically involves manual work, causing inter-operator variance. Here we introduce the Magia toolbox that enables processing of brain PET data with minimal user intervention. We investigated the accuracy of Magia with four tracers: [11C]carfentanil, [11C]raclopride, [11C]MADAM, and [11C]PiB. We used data from 30 control subjects for each tracer. Five operators manually delineated reference regions for each subject. The data were processed using Magia using the manually and automatically generated reference regions. We first assessed inter-operator variance resulting from the manual delineation of reference regions. We then compared the differences between the manually and automatically produced reference regions and the subsequently obtained binding potentials and standardized-uptake-value-ratios. The results show that manually produced reference regions can be remarkably different from each other, leading to substantial differences also in outcome measures. While the Magia-derived reference regions were anatomically different from the manual ones, Magia produced outcome measures highly consistent with the average of the manually obtained estimates. For [11C]carfentanil and [11C]PiB there was no bias, while for [11C]raclopride and [11C]MADAM Magia produced 3–5% higher binding potentials. Based on these results and considering the high inter-operator variance of the manual method, we conclude that Magia can be reliably used to process brain PET data.

中文翻译:

Magia:用于 PET 神经信息学的强大自动化图像处理和动力学建模工具箱

正电子发射断层扫描 (PET) 数据的处理通常涉及手动工作,导致操作员间差异。在这里,我们介绍了 Magia 工具箱,它能够以最少的用户干预处理大脑 PET 数据。我们使用四种示踪剂研究了 Magia 的准确性:[11C] 卡芬太尼、[11C] raclopride、[11C]MADAM 和 [11C]PiB。我们为每个示踪剂使用了 30 个对照受试者的数据。五个操作员手动描绘每个主题的参考区域。使用 Magia 使用手动和自动生成的参考区域处理数据。我们首先评估了手动划定参考区域导致的操作员间差异。然后,我们比较了手动和自动生成的参考区域与随后获得的结合电位和标准化摄取值比率之间的差异。结果表明,手动生成的参考区域可能彼此显着不同,导致结果测量也存在显着差异。虽然 Magia 衍生的参考区域在解剖学上与手动区域不同,但 Magia 产生的结果测量值与手动获得的估计值的平均值高度一致。[11C]卡芬太尼和[11C]PiB没有偏倚,而[11C]raclopride和[11C]MADAM Magia的结合电位高出3-5%。基于这些结果并考虑到手动方法的高操作员间差异,我们得出结论,Magia 可以可靠地用于处理大脑 PET 数据。结果表明,手动生成的参考区域可能彼此显着不同,导致结果测量也存在显着差异。虽然 Magia 衍生的参考区域在解剖学上与手动区域不同,但 Magia 产生的结果测量值与手动获得的估计值的平均值高度一致。[11C]卡芬太尼和[11C]PiB没有偏倚,而[11C]raclopride和[11C]MADAM Magia的结合电位高出3-5%。基于这些结果并考虑到手动方法的高操作员间差异,我们得出结论,Magia 可以可靠地用于处理大脑 PET 数据。结果表明,手动生成的参考区域可能彼此显着不同,导致结果测量也存在显着差异。虽然 Magia 衍生的参考区域在解剖学上与手动区域不同,但 Magia 产生的结果测量值与手动获得的估计值的平均值高度一致。[11C]卡芬太尼和[11C]PiB没有偏倚,而[11C]raclopride和[11C]MADAM Magia的结合电位高出3-5%。基于这些结果并考虑到手动方法的高操作员间差异,我们得出结论,Magia 可以可靠地用于处理大脑 PET 数据。虽然 Magia 衍生的参考区域在解剖学上与手动区域不同,但 Magia 产生的结果测量值与手动获得的估计值的平均值高度一致。[11C]卡芬太尼和[11C]PiB没有偏倚,而[11C]raclopride和[11C]MADAM Magia的结合电位高出3-5%。基于这些结果并考虑到手动方法的高操作员间差异,我们得出结论,Magia 可以可靠地用于处理大脑 PET 数据。虽然 Magia 衍生的参考区域在解剖学上与手动区域不同,但 Magia 产生的结果测量值与手动获得的估计值的平均值高度一致。[11C]卡芬太尼和[11C]PiB没有偏倚,而[11C]raclopride和[11C]MADAM Magia的结合电位高出3-5%。基于这些结果并考虑到手动方法的高操作员间差异,我们得出结论,Magia 可以可靠地用于处理大脑 PET 数据。
更新日期:2020-02-04
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