Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies

Background: : Centiloid was introduced to harmonise β-Amyloid (Aβ) PET quantification across different tracers, scanners and analysis techniques. Unfortunately, Centiloid still suffers from some quantification disparities in longitudinal analysis when normalising data from different tracers or scann...

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Main Authors: Pierrick Bourgeat, Vincent Doré, James Doecke, David Ames, Colin L. Masters, Christopher C. Rowe, Jurgen Fripp, Victor L. Villemagne
Format: Article
Language:English
Published: Elsevier 2021-02-01
Series:NeuroImage
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811920310788
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spelling doaj-f8adb122c9534af4aa3868725cd5b06f2020-12-03T04:29:41ZengElsevierNeuroImage1095-95722021-02-01226117593Non-negative matrix factorisation improves Centiloid robustness in longitudinal studiesPierrick Bourgeat0Vincent Doré1James Doecke2David Ames3Colin L. Masters4Christopher C. Rowe5Jurgen Fripp6Victor L. Villemagne7CSIRO Health and Biosecurity, Brisbane, Australia; Correspondence to: Level 5 UQ Health Sciences Building, Royal Brisbane and Women's Hospital, Herston, Queensland 4029, Australia.CSIRO Health and Biosecurity, Brisbane, Australia; Department of Molecular Imaging & Therapy, Austin Health, Melbourne, AustraliaCSIRO Health and Biosecurity, Brisbane, AustraliaUniversity of Melbourne, Academic Unit for Psychiatry of Old Age, St George's Hospital, Kew, AustraliaThe Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, AustraliaDepartment of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, AustraliaCSIRO Health and Biosecurity, Brisbane, AustraliaDepartment of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, AustraliaBackground: : Centiloid was introduced to harmonise β-Amyloid (Aβ) PET quantification across different tracers, scanners and analysis techniques. Unfortunately, Centiloid still suffers from some quantification disparities in longitudinal analysis when normalising data from different tracers or scanners. In this work, we aim to reduce this variability using a different analysis technique applied to the existing calibration data. Method: : All PET images from the Centiloid calibration dataset, along with 3762 PET images from the AIBL study were analysed using the recommended SPM pipeline. The PET images were SUVR normalised using the whole cerebellum. All SUVR normalised PiB images from the calibration dataset were decomposed using non-negative matrix factorisation (NMF). The NMF coefficients related to the first component were strongly correlated with global SUVR and were subsequently used as a surrogate for Aβ retention. For each tracer of the calibration dataset, the components of the NMF were computed in a way such that the coefficients of the first component would match those of the corresponding PiB. Given the strong correlations between the SUVR and the NMF coefficients on the calibration dataset, all PET images from AIBL were subsequently decomposed using the computed NMF, and their coefficients transformed into Centiloids. Results: : Using the AIBL data, the correlation between the standard Centiloid and the novel NMF-based Centiloid was high in each tracer. The NMF-based Centiloids showed a reduction of outliers, and improved longitudinal consistency. Furthermore, it removed the effects of switching tracers from the longitudinal variance of the Centiloid measure, when assessed using a linear mixed effects model. Conclusion: : We here propose a novel image driven method to perform the Centiloid quantification. The methods is highly correlated with standard Centiloids while improving the longitudinal reliability when switching tracers. Implementation of this method across multiple studies may lend to more robust and comparable data for future research.http://www.sciencedirect.com/science/article/pii/S1053811920310788CentiloidAβ Imaging
collection DOAJ
language English
format Article
sources DOAJ
author Pierrick Bourgeat
Vincent Doré
James Doecke
David Ames
Colin L. Masters
Christopher C. Rowe
Jurgen Fripp
Victor L. Villemagne
spellingShingle Pierrick Bourgeat
Vincent Doré
James Doecke
David Ames
Colin L. Masters
Christopher C. Rowe
Jurgen Fripp
Victor L. Villemagne
Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies
NeuroImage
Centiloid
Aβ Imaging
author_facet Pierrick Bourgeat
Vincent Doré
James Doecke
David Ames
Colin L. Masters
Christopher C. Rowe
Jurgen Fripp
Victor L. Villemagne
author_sort Pierrick Bourgeat
title Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies
title_short Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies
title_full Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies
title_fullStr Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies
title_full_unstemmed Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies
title_sort non-negative matrix factorisation improves centiloid robustness in longitudinal studies
publisher Elsevier
series NeuroImage
issn 1095-9572
publishDate 2021-02-01
description Background: : Centiloid was introduced to harmonise β-Amyloid (Aβ) PET quantification across different tracers, scanners and analysis techniques. Unfortunately, Centiloid still suffers from some quantification disparities in longitudinal analysis when normalising data from different tracers or scanners. In this work, we aim to reduce this variability using a different analysis technique applied to the existing calibration data. Method: : All PET images from the Centiloid calibration dataset, along with 3762 PET images from the AIBL study were analysed using the recommended SPM pipeline. The PET images were SUVR normalised using the whole cerebellum. All SUVR normalised PiB images from the calibration dataset were decomposed using non-negative matrix factorisation (NMF). The NMF coefficients related to the first component were strongly correlated with global SUVR and were subsequently used as a surrogate for Aβ retention. For each tracer of the calibration dataset, the components of the NMF were computed in a way such that the coefficients of the first component would match those of the corresponding PiB. Given the strong correlations between the SUVR and the NMF coefficients on the calibration dataset, all PET images from AIBL were subsequently decomposed using the computed NMF, and their coefficients transformed into Centiloids. Results: : Using the AIBL data, the correlation between the standard Centiloid and the novel NMF-based Centiloid was high in each tracer. The NMF-based Centiloids showed a reduction of outliers, and improved longitudinal consistency. Furthermore, it removed the effects of switching tracers from the longitudinal variance of the Centiloid measure, when assessed using a linear mixed effects model. Conclusion: : We here propose a novel image driven method to perform the Centiloid quantification. The methods is highly correlated with standard Centiloids while improving the longitudinal reliability when switching tracers. Implementation of this method across multiple studies may lend to more robust and comparable data for future research.
topic Centiloid
Aβ Imaging
url http://www.sciencedirect.com/science/article/pii/S1053811920310788
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