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

Article type: Research Article

Authors: Raman, Fabio^{a; b; e; f} | Grandhi, Sameera^{a; b} | Murchison, Charles F.^{b; c} | Kennedy, Richard E.^{b; c} | Landau, Susan^d | Roberson, Erik D.^{b; e; f} | McConathy, Jonathan^{a; b; *} | Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA | [b] Alzheimer’s Disease Center, University of Alabama at Birmingham, Birmingham, AL, USA | [c] Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA | [d] Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, CA, USA | [e] Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA | [f] Center for Neurodegeneration and Experimental Therapeutics, University of Alabama at Birmingham, Birmingham, AL, USA

Correspondence: [*] Correspondence to: Jonathan McConathy, MD, PhD, Department of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35249, USA. Tel.: +1 205 934 1388; E-mail: [email protected].

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: Background:Tools for efficient evaluation of amyloid- and tau-PET images are needed in both clinical and research settings. Objective: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. Methods: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. Results: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. Conclusions:BLAzER provides an efficient methodology for regional brain PET quantification. FDA-cleared components and visualization of registration reduce barriers between research and clinical applications.

Keywords: [¹⁸F]AV-45, [¹⁸F]AV-1451, amyloid-β , brain segmentation, neuroimaging, positron emission tomography, tau

DOI: 10.3233/JAD-190329

Journal: Journal of Alzheimer's Disease, vol. 70, no. 4, pp. 1241-1257, 2019