ArticleNeuroImage2009
Reducing between scanner differences in multi-center PET studies.
Article in NeuroImage, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 157 papers.
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Who cites it
157 citing papers in PubMed.
- Trial
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- Combination of automated brain volumetry on MRI and quantitative tau deposition on THK-5351 PET to support diagnosis of Alzheimer's disease.Scientific reports · 2021Trial
- Measuring in-vivo tau pathology in the basal forebrain with PET imaging.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Modelling the temporal evolution of plasma p-tau217, amyloid PET, tau PET and cognition.Brain : a journal of neurology · 2026Article
- The Centiloid Scale in Amyloid PET Imaging: Current Role in Alzheimer's Disease Diagnosis, Treatment Planning, and Monitoring During Anti-Amyloid Therapy: A Clinical Perspective.Diagnostics (Basel, Switzerland) · 2026Review
- Longitudinal subcortical volume changes and their correlations with multiple PET and fluid biomarkers in dominantly inherited Alzheimer's disease.The journal of prevention of Alzheimer's disease · 2026Observational
- Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET.Human brain mapping · 2026Article
- Plasma Aβ42/40 predicts progression from Aβ-amyloid negative to positive PET scans.The journal of prevention of Alzheimer's disease · 2026Article
- AI-enhanced Centiloid quantification of amyloid PET images.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Visual interpretation of [European journal of nuclear medicine and molecular imaging · 2026Article
- Predictive Value of Plasma Biomarkers in Tau-PET Transitions.Annals of neurology · 2025Article
- The Knight Alzheimer Research Imaging (KARI) dataset: a comprehensive multimodal resource for exploring aging, preclinical, and symptomatic Alzheimer disease pathology.Research square · 2025Article
- DG-DiT: Dual-Branch Gating Diffusion Transformer for Multi-Tracer and Multi-Scanner Brain PET Image Denoising.IEEE transactions on radiation and plasma medical sciences · 2025Article
- Measurement accuracy and practicality ofAnnals of nuclear medicine · 2025Article
- Development and evaluation of image preprocessing pipelines for the Centiloid method on Down Syndrome data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Investigating dopaminergic abnormalities in schizophrenia and first-episode psychosis with normative modelling and multisite molecular neuroimaging.Molecular psychiatry · 2025Article
- Data-Driven Image-Based Protocol for Brain PET Image Harmonization.Sensors (Basel, Switzerland) · 2025Article
- Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer's disease.Scientific reports · 2025Article
97 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This work is part of the multi-center Alzheimer's Disease Neuroimaging Initiative (ADNI), a large multi-site study of dementia, including patients having mild cognitive impairment (MCI), probable Alzheimer's disease (AD), as well as healthy elderly controls. A major portion of ADNI involves the use of [(18)F]-fluorodeoxyglucose (FDG) with positron emission tomography (PET). The objective of this paper is the reduction of inter-scanner differences in the FDG-PET scans obtained from the 50 participating PET centers having fifteen different scanner models. In spite of a standardized imaging protocol, systematic inter-scanner variability in PET images from various sites is observed primarily due to differences in scanner resolution, reconstruction techniques, and different implementations of scatter and attenuation corrections. Two correction steps were developed by comparison of 3-D Hoffman brain phantom scans with the 'gold standard' digital 3-D Hoffman brain phantom: i) high frequency correction; where a smoothing kernel for each scanner model was estimated to smooth all images to a common resolution and ii) low frequency correction; where smooth affine correction factors were obtained to reduce the attenuation and scatter correction errors. For the phantom data, the high frequency correction reduced the variability by 20%-50% and the low frequency correction further reduced the differences by another 20%-25%. Correction factors obtained from phantom studies were applied to 95 scans from normal control subjects obtained from the participating sites. The high frequency correction reduced differences similar to the phantom studies. However, the low frequency correction did not further reduce differences; hence further refinement of the procedure is necessary.
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Registered trials
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