Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA.
Alzheimer's Disease Center, University of Alabama at Birmingham, Birmingham, AL, USA.
J Alzheimers Dis. 2019;70(4):1241-1257. doi: 10.3233/JAD-190329.
Tools for efficient evaluation of amyloid- and tau-PET images are needed in both clinical and research settings.
This study was designed to validate a semi-automated image analysis methodology, called Biomarker Localization, Analysis, Visualization, Extraction, and Registration (BLAzER). We tested BLAzER using two different segmentation platforms, FreeSurfer (FS) and Neuroreader (NR), for regional brain PET quantification in participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
127 amyloid-PET and 55 tau-PET studies with volumetric MRIs were obtained from ADNI. The BLAzER methodology utilizes segmentation of MR images by FS or NR, then visualizes and quantifies regional brain PET data using FDA-cleared software (MIM), enabling quality control to ensure optimal registration and to detect segmentation errors.
BLAzER analysis required ∼5 min plus segmentation time. BLAzER using FS segmentation showed strong agreement with ADNI for global amyloid-PET standardized uptake value ratios (SUVRs) (r = 0.9922, p < 0.001) and regional tau-PET SUVRs across all Braak staging regions (r > 0.97, p < 0.001) with high inter-operator reproducibility (ICC > 0.97) and nearly identical dichotomization as amyloid-positive or -negative (2 discrepant cases out of 127). Comparing FS versus NR segmentation with BLAzER, global SUVRs were strongly correlated for amyloid-PET (r = 0.9841, p < 0.001), but were systematically higher (4% on average) with NR, likely due to more inclusion of white matter with NR-defined regions.
BLAzER provides an efficient methodology for regional brain PET quantification. FDA-cleared components and visualization of registration reduce barriers between research and clinical applications.
在临床和研究环境中都需要有效的工具来评估淀粉样蛋白和 tau-PET 图像。
本研究旨在验证一种半自动图像分析方法，称为生物标志物定位、分析、可视化、提取和注册（BLAzER）。我们使用两种不同的分割平台，FreeSurfer（FS）和 Neuroreader（NR），在阿尔茨海默病神经影像学倡议（ADNI）数据集的参与者中进行了 BLAzER 的测试，以进行区域性脑 PET 定量。
从 ADNI 获得了 127 项淀粉样蛋白-PET 和 55 项 tau-PET 研究，这些研究都有容积 MRI。BLAzER 方法利用 FS 或 NR 对 MRI 进行分割，然后使用经 FDA 批准的软件（MIM）可视化和量化区域性脑 PET 数据，从而进行质量控制，以确保最佳注册并检测分割错误。
BLAzER 分析大约需要 5 分钟加分割时间。使用 FS 分割的 BLAzER 与 ADNI 对全局淀粉样蛋白-PET 标准化摄取比值（SUVR）（r=0.9922，p<0.001）和所有 Braak 分期区域的区域性 tau-PET SUVR 具有很强的一致性（r>0.97，p<0.001），具有较高的操作员间可重复性（ICC>0.97），并且与淀粉样蛋白阳性或阴性的二分法几乎相同（127 例中有 2 例不一致）。比较 FS 与 BLAzER 的 NR 分割，全局 SUVR 与淀粉样蛋白 PET 高度相关（r=0.9841，p<0.001），但 NR 分割的 SUVR 系统更高（平均高 4%），这可能是由于 NR 定义的区域更多地包含了白质。
BLAzER 为区域性脑 PET 定量提供了一种有效的方法。经 FDA 批准的组件和注册可视化减少了研究和临床应用之间的障碍。
