Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/25438
Title: Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies.
Austin Authors: Bourgeat, Pierrick;Doré, Vincent ;Doecke, James;Ames, David;Masters, Colin L ;Rowe, Christopher C ;Fripp, Jurgen;Villemagne, Victor L 
Affiliation: CSIRO Health and Biosecurity, Brisbane, Australia
University of Melbourne, Academic Unit for Psychiatry of Old Age, St George's Hospital, Kew, Australia
The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia
Molecular Imaging and Therapy
Department of Medicine, University of Melbourne, Melbourne, Australia
Issue Date: 2021
metadata.dc.date: 2020-11-26
Publication information: NeuroImage 2021; 226: 117593
Abstract: 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. 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. 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. 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.
URI: https://ahro.austin.org.au/austinjspui/handle/1/25438
DOI: 10.1016/j.neuroimage.2020.117593
PubMed URL: 33248259
Type: Journal Article
Subjects: Aβ Imaging
Centiloid
Appears in Collections:Journal articles

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