CSIRO Health and Biosecurity, Brisbane, Australia.
CSIRO Health and Biosecurity, Brisbane, Australia; Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia.
Neuroimage. 2021 Feb 1;226:117593. doi: 10.1016/j.neuroimage.2020.117593. Epub 2020 Nov 26.
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.
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 factorisation (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.
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.
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.
Centiloid 的引入旨在协调不同示踪剂、扫描仪和分析技术之间的β-淀粉样蛋白（Aβ）PET 定量。不幸的是，当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定量差异。在这项工作中，我们旨在通过应用于现有校准数据的不同分析技术来减少这种可变性。
使用推荐的 SPM 流水线分析 Centiloid 校准数据集的所有 PET 图像以及 AIBL 研究的 3762 个 PET 图像。使用全小脑对 PET 图像进行 SUVR 归一化。使用非负矩阵分解（NMF）对校准数据集的所有 SUVR 归一化 PiB 图像进行分解。与全局 SUVR 强烈相关的 NMF 系数的第一分量与 Aβ保留相关，并且可以作为替代物使用。对于校准数据集中的每种示踪剂，以这样的方式计算 NMF 的分量，即第一分量的系数将与相应 PiB 的系数匹配。鉴于校准数据集上 SUVR 和 NMF 系数之间的强相关性，使用计算出的 NMF 对 AIBL 的所有 PET 图像进行分解，并且将其系数转换为 Centiloids。
使用 AIBL 数据，在每种示踪剂中，标准 Centiloid 和基于新的 NMF 的 Centiloid 之间的相关性都很高。基于 NMF 的 Centiloids 减少了离群值，并提高了纵向一致性。此外，当使用线性混合效应模型评估时，它从 Centiloid 测量的纵向方差中去除了切换示踪剂的影响。
我们在这里提出了一种新的基于图像的方法来执行 Centiloid 定量。该方法与标准 Centiloid 高度相关，同时在切换示踪剂时提高了纵向可靠性。在多个研究中实施此方法可能会为未来的研究提供更稳健和可比的数据。
