Validation of FDG-PET datasets of normal controls for the extraction of SPM-based brain metabolism maps
Validation of FDG-PET datasets of normal controls for the extraction of SPM-based brain metabolism maps
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DOI：
10.1007/s00259-020-05175-1
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发表时间：
2021-01-10
影响因子：
9.1
通讯作者：
Perani, Daniela
中科院分区：
文献类型：
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作者：
Caminiti, Silvia Paola;Sala, Arianna;Perani, Daniela
一个适当的健康对照数据集是强制性的，以实现良好的性能，在体素分析。我们的目的是评估[18 F]FDG PET健康对照（HC）的大脑数据集，基于公开的数据，提取基于体素的大脑代谢maps在单一subjectlevel.Methods HC图像的选择是基于视觉评级，库克的距离和折刀分析后，排除伪影和/或离群值。通过Dice评分分析，与标准参考HC数据集（HSR-HC）相比，测试了这些HC数据集（ADNI-HC和AIMN-HC）提取单个患者代谢低下模式的性能。我们评价了不同HC数据集在三个独立患者队列（即ADD、bvFTD和DLB）中评估单受试者SPM代谢低下的性能和可比性。结果两步Cook距离分析和随后的折刀分析从AIMN-HC数据集中选择了n = 125例受试者，从ADNI-HC数据集中选择了n = 75例受试者。使用新数据集获得的3个患者队列中SPM代谢减退t图与HSR-HC标准参考数据集相比的平均一致性为AIMN-HC数据集0.87，ADNI-HC数据集0.83。模式表达分析显示高的整体准确性（> 80%）的SPM的t图分类根据不同的统计阈值和sample sizes.Conclusions应用程序确保这些HC数据集的有效性，为单受试者估计脑代谢使用体素明智的比较。这些精心挑选的HC数据集可随时用于研究和临床环境。
Purpose An appropriate healthy control dataset is mandatory to achieve good performance in voxel-wise analyses. We aimed at evaluating [18F]FDG PET brain datasets of healthy controls (HC), based on publicly available data, for the extraction of voxel-based brain metabolism maps at the single-subject level.Methods Selection of HC images was based on visual rating, after Cook's distance and jack-knife analyses, to exclude artefacts and/or outliers. The performance of these HC datasets (ADNI-HC and AIMN-HC) to extract hypometabolism patterns in single patients was tested in comparison with the standard reference HC dataset (HSR-HC) by means of Dice score analysis. We evaluated the performance and comparability of the different HC datasets in the assessment of single-subject SPM-based hypometabolism in three independent cohorts of patients, namely, ADD, bvFTD and DLB.Results Two-step Cook's distance analysis and the subsequent jack-knife analysis resulted in the selection of n = 125 subjects from the AIMN-HC dataset and n = 75 subjects from the ADNI-HC dataset. The average concordance between SPM hypometabolism t-maps in the three patient cohorts, as obtained with the new datasets and compared to the HSR-HC standard reference dataset, was 0.87 for the AIMN-HC dataset and 0.83 for the ADNI-HC dataset. Pattern expression analysis revealed high overall accuracy (> 80%) of the SPM t-map classification according to different statistical thresholds and sample sizes.Conclusions The applied procedures ensure validity of these HC datasets for the single-subject estimation of brain metabolism using voxel-wise comparisons. These well-selected HC datasets are ready-to-use in research and clinical settings.