Biomarker Localization, Analysis, Visualization, Extraction, and Registration (BLAzER) Workflow for Research and Clinical Brain PET Applications

Academic Article


  • ABSTRACT Objective There is a need for tools enabling efficient evaluation of amyloid- and tau-PET images suited for both clinical and research settings. The purpose of this study was to assess and validate a semi-automated imaging workflow, 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 images from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Methods 127 amyloid-PET and 55 tau-PET studies along with corresponding volumetric MRI were obtained from ADNI. The BLAzER workflow 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 detect segmentation errors. Results BLAzER analysis required only ∼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 for both (ICC > 0.97) and nearly identical dichotomization as amyloid-positive or -negative (2 discrepant cases out of 127). Comparing FS vs. NR segmentation with BLAzER, the global SUVRs were strongly correlated for global 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, which has high florbetapir binding. Conclusions BLAzER provides an efficient workflow for regional brain PET quantification. FDA-cleared components and the ability to visualize registration reduce barriers between research and clinical applications.
  • Keywords

  • Alzheimer’s Disease Neuroimaging Initiative
  • Digital Object Identifier (doi)

    Author List

  • Raman F; Grandhi S; Murchison C; Kennedy R; Landau S; Roberson E; McConathy J; Alzheimer’s Disease Neuroimaging Initiative