Pierrick Bourgeat1, Vincent Doré2, James Doecke3, David Ames4, Colin L Masters5, Christopher C Rowe6, Jurgen Fripp3, Victor L Villemagne6. 1. CSIRO Health and Biosecurity, Brisbane, Australia. Electronic address: Pierrick.Bourgeat@csiro.au. 2. CSIRO Health and Biosecurity, Brisbane, Australia; Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia. 3. CSIRO Health and Biosecurity, Brisbane, Australia. 4. University of Melbourne, Academic Unit for Psychiatry of Old Age, St George's Hospital, Kew, Australia. 5. The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, Australia. 6. Department of Molecular Imaging & Therapy, Austin Health, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, Australia.
Abstract
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.
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.
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Authors: Mark R Battle; Lovena Chedumbarum Pillay; Val J Lowe; David Knopman; Bradley Kemp; Christopher C Rowe; Vincent Doré; Victor L Villemagne; Christopher J Buckley Journal: EJNMMI Res Date: 2018-12-05 Impact factor: 3.138
Authors: Pierrick Bourgeat; Vincent Doré; Samantha C Burnham; Tammie Benzinger; Duygu Tosun; Shenpeng Li; Manu Goyal; Pamela LaMontagne; Liang Jin; Christopher C Rowe; Michael W Weiner; John C Morris; Colin L Masters; Jurgen Fripp; Victor L Villemagne Journal: Neuroimage Date: 2022-07-30 Impact factor: 7.400
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