BACKGROUND Centiloid was introduced to harmonise β-Amyloid (Aβ) PET quantification across different tracers, scanners and analysis techniques. Unfortunately, Centiloid still suffers from some quantification disparities in longitudinal analysis when normalising data from different tracers or scanners. In this work, we aim to reduce this variability using a different analysis technique applied to the existing calibration data. METHOD All PET images from the Centiloid calibration dataset, along with 3762 PET images from the AIBL study were analysed using the recommended SPM pipeline. The PET images were SUVR normalised using the whole cerebellum. All SUVR normalised PiB images from the calibration dataset were decomposed using non-negative matrix factorization (NMF). The NMF coefficients related to the first component were strongly correlated with global SUVR and were subsequently used as a surrogate for Aβ retention. For each tracer of the calibration dataset, the components of the NMF were computed in a way such that the coefficients of the first component would match those of the corresponding PiB. Given the strong correlations between the SUVR and the NMF coefficients on the calibration dataset, all PET images from AIBL were subsequently decomposed using the computed NMF, and their coefficients transformed into Centiloids. RESULTS Using the AIBL data, the correlation between the standard Centiloid and the novel NMF-based Centiloid was high in each tracer. The NMF-based Centiloids showed a reduction of outliers, and improved longitudinal consistency. Furthermore, it removed the effects of switching tracers from the longitudinal variance of the Centiloid measure, when assessed using a linear mixed effects model. CONCLUSION We here propose a novel image driven method to perform the Centiloid quantification. The methods is highly correlated with standard Centiloids while improving the longitudinal reliability when switching tracers. Implementation of this method across multiple studies may lend to more robust and comparable data for future research.

中文翻译：

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非负矩阵分解提高纵向研究中的 Centiloid 稳健性

背景 Centiloid 被引入以协调不同示踪剂、扫描仪和分析技术之间的 β-淀粉样蛋白 (Aβ) PET 量化。不幸的是，在对来自不同示踪剂或扫描仪的数据进行归一化时，Centiloid 在纵向分析中仍然存在一些量化差异。在这项工作中，我们的目标是使用应用于现有校准数据的不同分析技术来减少这种可变性。方法 使用推荐的 SPM 管道分析来自 Centiloid 校准数据集的所有 PET 图像以及来自 AIBL 研究的 3762 幅 PET 图像。使用整个小脑对 PET 图像进行 SUVR 标准化。来自校准数据集的所有 SUVR 归一化 PiB 图像都使用非负矩阵分解 (NMF) 进行分解。与第一个成分相关的 NMF 系数与全球 SUVR 密切相关，随后被用作 Aβ 保留的替代指标。对于校准数据集的每个示踪剂，NMF 的组件的计算方式使得第一个组件的系数与相应 PiB 的系数相匹配。鉴于校准数据集上 SUVR 和 NMF 系数之间的强相关性，随后使用计算的 NMF 分解来自 AIBL 的所有 PET 图像，并将它们的系数转换为 Centiloids。结果 使用 AIBL 数据，标准 Centiloid 和基于 NMF 的新型 Centiloid 之间的相关性在每个示踪剂中都很高。基于 NMF 的 Centiloids 减少了异常值，并提高了纵向一致性。此外，当使用线性混合效应模型进行评估时，它从 Centiloid 度量的纵向方差中去除了切换示踪剂的影响。结论我们在这里提出了一种新的图像驱动方法来执行 Centiloid 量化。这些方法与标准 Centiloids 高度相关，同时提高了切换示踪剂时的纵向可靠性。在多项研究中实施这种方法可能会为未来的研究提供更可靠和可比的数据。