TY - JOUR
T1 - rPOP
T2 - Robust PET-only processing of community acquired heterogeneous amyloid-PET data
AU - Alzheimer's Disease Neuroimaging Initiative
AU - Iaccarino, Leonardo
AU - La Joie, Renaud
AU - Koeppe, Robert
AU - Siegel, Barry A.
AU - Hillner, Bruce E.
AU - Gatsonis, Constantine
AU - Whitmer, Rachel A.
AU - Carrillo, Maria C.
AU - Apgar, Charles
AU - Camacho, Monica R.
AU - Nosheny, Rachel
AU - Rabinovici, Gil D.
N1 - Funding Information:
The authors wish to thank Susan Landau, Tyler Ward, Alice Murphy and Bill Jagust for the insightful discussions and support. The present study was supported by National Institutes of Health (to RLJ, NIH/NIA K99-AG065501; to GDR NIH/NIA R35-AG072362), Alzheimer's Association (to GDR, ZEN-21-848216; to GDR and LI, SG-21-876655). Dr. Siegel receives research support from the American College of Radiology, Blue Earth Diagnostics, LLC, ImagingAb, Inc. and Progenics Pharmaceuticals; consulting fees from the American College of Radiology, American Medical Foundation for Peer Review & Education, Avid Radiopharmaceuticals, Inc. Capella Imaging, LLC, Curium Pharma, GE Healthcare, Lantheus Medical Imaging, Inc. and the Radiological Society of North America; and lecture honoraria from Siemens Healthineers (spouse). Dr. Nosheny receives funding from the National Institute of Aging, Genentech Inc. California Department of Public Health. Dr. Rabinovici receives research support from Avid Radiopharmaceuticals Inc, GE Healthcare, and Life Molecular Imaging via the IDEAS study, and from Genentech. He has received consulting fees from Eisai, Genentech, Johnson & Johnson and Roche. The IDEAS Study and this work were funded by the Alzheimer's Association, the American College of Radiology, Avid Radiopharmaceuticals Inc, GE Healthcare, and Life Molecular Imaging (formerly Piramal Imaging). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co. Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Leonardo Iaccarino: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, Writing - review & editing, Renaud La Joie: Conceptualization, Investigation, Methodology, Validation, Visualization, Writing - review & editing, Robert Koeppe: Conceptualization, Methodology, Validation, Writing - review & editing, Barry A. Siegel: Funding acquisition, Writing - review & editing, Bruce E. Hillner: Funding acquisition, Writing - review & editing, Constantine Gatsonis: Data curation, Funding acquisition, Project administration, Writing - review & editing, Rachel A. Whitmer: Funding acquisition, Writing - review & editing, Maria C. Carrillo: Funding acquisition, Writing - review & editing, Charles Apgar: Data curation, Funding acquisition, Project administration, Writing - review & editing, Monica R. Camacho: Data curation, Project administration, Writing - review & editing, Rachel Nosheny: Data curation, Funding acquisition, Investigation, Project administration, Writing - review & editing, Gil D. Rabinovici: Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Writing - review & editing
Funding Information:
The authors wish to thank Susan Landau, Tyler Ward, Alice Murphy and Bill Jagust for the insightful discussions and support. The present study was supported by National Institutes of Health (to RLJ, NIH/NIA K99-AG065501 ; to GDR NIH/NIA R35-AG072362 ), Alzheimer's Association (to GDR, ZEN-21-848216 ; to GDR and LI, SG-21-876655 ). Dr. Siegel receives research support from the American College of Radiology , Blue Earth Diagnostics, LLC, ImagingAb, Inc. and Progenics Pharmaceuticals; consulting fees from the American College of Radiology, American Medical Foundation for Peer Review & Education, Avid Radiopharmaceuticals, Inc., Capella Imaging, LLC, Curium Pharma, GE Healthcare, Lantheus Medical Imaging, Inc. and the Radiological Society of North America; and lecture honoraria from Siemens Healthineers (spouse). Dr. Nosheny receives funding from the National Institute of Aging, Genentech Inc., California Department of Public Health. Dr. Rabinovici receives research support from Avid Radiopharmaceuticals Inc, GE Healthcare, and Life Molecular Imaging via the IDEAS study, and from Genentech. He has received consulting fees from Eisai, Genentech, Johnson & Johnson and Roche. The IDEAS Study and this work were funded by the Alzheimer's Association, the American College of Radiology, Avid Radiopharmaceuticals Inc, GE Healthcare, and Life Molecular Imaging (formerly Piramal Imaging). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) ( National Institutes of Health Grant U01 AG024904 ) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging , the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Publisher Copyright:
© 2021
PY - 2022/2/1
Y1 - 2022/2/1
N2 - The reference standard for amyloid-PET quantification requires structural MRI (sMRI) for preprocessing in both multi-site research studies and clinical trials. Here we describe rPOP (robust PET-Only Processing), a MATLAB-based MRI-free pipeline implementing non-linear warping and differential smoothing of amyloid-PET scans performed with any of the FDA-approved radiotracers (18F-florbetapir/FBP, 18F-florbetaben/FBB or 18F-flutemetamol/FLUTE). Each image undergoes spatial normalization based on weighted PET templates and data-driven differential smoothing, then allowing users to perform their quantification of choice. Prior to normalization, users can choose whether to automatically reset the origin of the image to the center of mass or proceed with the pipeline with the image as it is. We validate rPOP with n = 740 (514 FBP, 182 FBB, 44 FLUTE) amyloid-PET scans from the Imaging Dementia—Evidence for Amyloid Scanning – Brain Health Registry sub-study (IDEAS-BHR) and n = 1,518 scans from the Alzheimer's Disease Neuroimaging Initiative (n = 1,249 FBP, n = 269 FBB), including heterogeneous acquisition and reconstruction protocols. After running rPOP, a standard quantification to extract Standardized Uptake Value ratios and the respective Centiloids conversion was performed. rPOP-based amyloid status (using an independent pathology-based threshold of ≥24.4 Centiloid units) was compared with either local visual reads (IDEAS-BHR, n = 663 with complete valid data and reads available) or with amyloid status derived from an MRI-based PET processing pipeline (ADNI, thresholds of >20/>18 Centiloids for FBP/FBB). Finally, within the ADNI dataset, we tested the linear associations between rPOP- and MRI-based Centiloid values. rPOP achieved accurate warping for N = 2,233/2,258 (98.9%) in the first pass. Of the N = 25 warping failures, 24 were rescued with manual reorientation and origin reset prior to warping. We observed high concordance between rPOP-based amyloid status and both visual reads (IDEAS-BHR, Cohen's k = 0.72 [0.7–0.74], ∼86% concordance) or MRI-pipeline based amyloid status (ADNI, k = 0.88 [0.87–0.89], ∼94% concordance). rPOP- and MRI-pipeline based Centiloids were strongly linearly related (R2:0.95, p<0.001), with this association being significantly modulated by estimated PET resolution (β= -0.016, p<0.001). rPOP provides reliable MRI-free amyloid-PET warping and quantification, leveraging widely available software and only requiring an attenuation-corrected amyloid-PET image as input. The rPOP pipeline enables the comparison and merging of heterogeneous datasets and is publicly available at https://github.com/leoiacca/rPOP.
AB - The reference standard for amyloid-PET quantification requires structural MRI (sMRI) for preprocessing in both multi-site research studies and clinical trials. Here we describe rPOP (robust PET-Only Processing), a MATLAB-based MRI-free pipeline implementing non-linear warping and differential smoothing of amyloid-PET scans performed with any of the FDA-approved radiotracers (18F-florbetapir/FBP, 18F-florbetaben/FBB or 18F-flutemetamol/FLUTE). Each image undergoes spatial normalization based on weighted PET templates and data-driven differential smoothing, then allowing users to perform their quantification of choice. Prior to normalization, users can choose whether to automatically reset the origin of the image to the center of mass or proceed with the pipeline with the image as it is. We validate rPOP with n = 740 (514 FBP, 182 FBB, 44 FLUTE) amyloid-PET scans from the Imaging Dementia—Evidence for Amyloid Scanning – Brain Health Registry sub-study (IDEAS-BHR) and n = 1,518 scans from the Alzheimer's Disease Neuroimaging Initiative (n = 1,249 FBP, n = 269 FBB), including heterogeneous acquisition and reconstruction protocols. After running rPOP, a standard quantification to extract Standardized Uptake Value ratios and the respective Centiloids conversion was performed. rPOP-based amyloid status (using an independent pathology-based threshold of ≥24.4 Centiloid units) was compared with either local visual reads (IDEAS-BHR, n = 663 with complete valid data and reads available) or with amyloid status derived from an MRI-based PET processing pipeline (ADNI, thresholds of >20/>18 Centiloids for FBP/FBB). Finally, within the ADNI dataset, we tested the linear associations between rPOP- and MRI-based Centiloid values. rPOP achieved accurate warping for N = 2,233/2,258 (98.9%) in the first pass. Of the N = 25 warping failures, 24 were rescued with manual reorientation and origin reset prior to warping. We observed high concordance between rPOP-based amyloid status and both visual reads (IDEAS-BHR, Cohen's k = 0.72 [0.7–0.74], ∼86% concordance) or MRI-pipeline based amyloid status (ADNI, k = 0.88 [0.87–0.89], ∼94% concordance). rPOP- and MRI-pipeline based Centiloids were strongly linearly related (R2:0.95, p<0.001), with this association being significantly modulated by estimated PET resolution (β= -0.016, p<0.001). rPOP provides reliable MRI-free amyloid-PET warping and quantification, leveraging widely available software and only requiring an attenuation-corrected amyloid-PET image as input. The rPOP pipeline enables the comparison and merging of heterogeneous datasets and is publicly available at https://github.com/leoiacca/rPOP.
UR - http://www.scopus.com/inward/record.url?scp=85120981675&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2021.118775
DO - 10.1016/j.neuroimage.2021.118775
M3 - Article
C2 - 34890793
AN - SCOPUS:85120981675
VL - 246
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
M1 - 118775
ER -