Literature DB >> 25652817

Quantification of 18F-florbetapir PET: comparison of two analysis methods.

Chloe Hutton1, Jerome Declerck, Mark A Mintun, Michael J Pontecorvo, Michael D Devous, Abhinay D Joshi.   

Abstract

PURPOSE: (18)F-Florbetapir positron emission tomography (PET) can be used to image amyloid burden in the human brain. A previously developed research method has been shown to have a high test-retest reliability and good correlation between standardized uptake value ratio (SUVR) and amyloid burden at autopsy. The goal of this study was to determine how well SUVRs computed using the research method could be reproduced using an automatic quantification method, developed for clinical use.
METHODS: Two methods for the quantitative analysis of (18)F-florbetapir PET were compared in a diverse clinical population of 604 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and in a group of 74 younger healthy controls (YHC). Cortex to cerebellum SUVRs were calculated using the research method, which is based on SPM, yielding 'research SUVRs', and using syngo.PET Amyloid Plaque, yielding 'sPAP SUVRs'.
RESULTS: Mean cortical SUVRs calculated using the two methods for the 678 subjects were correlated (r = 0.99). Linear regression of sPAP SUVRs on research SUVRs was used to convert the research method SUVR threshold for florbetapir positivity of 1.10 to a corresponding threshold of 1.12 for sPAP. Using the corresponding thresholds, categorization of SUVR values were in agreement between research and sPAP SUVRs for 96.3 % of the ADNI images. SUVRs for all YHC were below the corresponding thresholds.
CONCLUSION: Automatic florbetapir PET quantification using sPAP yielded cortex to cerebellum SUVRs which were correlated and in good agreement with the well-established research method. The research SUVR threshold for florbetapir positivity was reliably converted to a corresponding threshold for sPAP SUVRs.

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Year:  2015        PMID: 25652817     DOI: 10.1007/s00259-015-2988-7

Source DB:  PubMed          Journal:  Eur J Nucl Med Mol Imaging        ISSN: 1619-7070            Impact factor:   9.236


  23 in total

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7.  The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET.

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8.  Performance characteristics of amyloid PET with florbetapir F 18 in patients with alzheimer's disease and cognitively normal subjects.

Authors:  Abhinay D Joshi; Michael J Pontecorvo; Chrisopher M Clark; Alan P Carpenter; Danna L Jennings; Carl H Sadowsky; Lee P Adler; Karel D Kovnat; John P Seibyl; Anupa Arora; Krishnendu Saha; Jason D Burns; Mark J Lowrey; Mark A Mintun; Daniel M Skovronsky
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10.  Comparison of MRI based and PET template based approaches in the quantitative analysis of amyloid imaging with PIB-PET.

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Review 4.  Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.

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5.  A new integrated dual time-point amyloid PET/MRI data analysis method.

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6.  Evaluation of software tools for automated identification of neuroanatomical structures in quantitative β-amyloid PET imaging to diagnose Alzheimer's disease.

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7.  Optimisation and usefulness of quantitative analysis of 18F-florbetapir PET.

Authors:  Daniel Fakhry-Darian; Neva Hiten Patel; Sairah Khan; Tara Barwick; William Svensson; Sameer Khan; Richard J Perry; Paresh Malhotra; Christopher J Carswell; Kuldip S Nijran; Zarni Win
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8.  Anomaly detection for the individual analysis of brain PET images.

Authors:  Ninon Burgos; M Jorge Cardoso; Jorge Samper-González; Marie-Odile Habert; Stanley Durrleman; Sébastien Ourselin; Olivier Colliot
Journal:  J Med Imaging (Bellingham)       Date:  2021-04-05

9.  Classification of amyloid status using machine learning with histograms of oriented 3D gradients.

Authors:  Liam Cattell; Günther Platsch; Richie Pfeiffer; Jérôme Declerck; Julia A Schnabel; Chloe Hutton
Journal:  Neuroimage Clin       Date:  2016-05-10       Impact factor: 4.881

10.  Regional brain amyloid-β accumulation associates with domain-specific cognitive performance in Parkinson disease without dementia.

Authors:  Rizwan S Akhtar; Sharon X Xie; Yin J Chen; Jacqueline Rick; Rachel G Gross; Ilya M Nasrallah; Vivianna M Van Deerlin; John Q Trojanowski; Alice S Chen-Plotkin; Howard I Hurtig; Andrew D Siderowf; Jacob G Dubroff; Daniel Weintraub
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