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Spill-in counts in the quantification of (18)F-florbetapir on Aβ-negative subjects: the effect of including white matter in the reference region

BACKGROUND: We aim to provide a systematic study of the impact of white matter (WM) spill-in on the calculation of standardized uptake value ratios (SUVRs) on Aβ-negative subjects, and we study the effect of including WM in the reference region as a compensation. In addition, different partial volum...

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Detalles Bibliográficos
Autores principales: López-González, Francisco Javier, Moscoso, Alexis, Efthimiou, Nikos, Fernández-Ferreiro, Anxo, Piñeiro-Fiel, Manuel, Archibald, Stephen J., Aguiar, Pablo, Silva-Rodríguez, Jesús
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923310/
https://www.ncbi.nlm.nih.gov/pubmed/31858289
http://dx.doi.org/10.1186/s40658-019-0258-7
Descripción
Sumario:BACKGROUND: We aim to provide a systematic study of the impact of white matter (WM) spill-in on the calculation of standardized uptake value ratios (SUVRs) on Aβ-negative subjects, and we study the effect of including WM in the reference region as a compensation. In addition, different partial volume correction (PVC) methods are applied and evaluated. METHODS: We evaluated magnetic resonance imaging and (18)F-AV-45 positron emission tomography data from 122 cognitively normal (CN) patients recruited at the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cortex SUVRs were obtained by using the cerebellar grey matter (CGM) (SUVR(CGM)) and the whole cerebellum (SUVR(WC)) as reference regions. The correlations between the different SUVRs and the WM uptake (WM-SUVR(CGM)) were studied in patients, and in a well-controlled framework based on Monte Carlo (MC) simulation. Activity maps for the MC simulation were derived from ADNI patients by using a voxel-wise iterative process (BrainViset). Ten WM uptakes covering the spectrum of WM values obtained from patient data were simulated for different patients. Three different PVC methods were tested (a) the regional voxel-based (RBV), (b) the iterative Yang (iY), and (c) a simplified analytical correction derived from our MC simulation. RESULTS: WM-SUVR(CGM) followed a normal distribution with an average of 1.79 and a standard deviation of 0.243 (13.6%). SUVR(CGM) was linearly correlated to WM-SUVR(CGM) (r = 0.82, linear fit slope = 0.28). SUVR(WC) was linearly correlated to WM-SUVR(CGM) (r = 0.64, linear fit slope = 0.13). Our MC results showed that these correlations are compatible with those produced by isolated spill-in effect (slopes of 0.23 and 0.11). The impact of the spill-in was mitigated by using PVC for SUVR(CGM) (slopes of 0.06 and 0.07 for iY and RBV), while SUVR(WC) showed a negative correlation with SUVR(CGM) after PVC. The proposed analytical correction also reduced the observed correlations when applied to patient data (r = 0.27 for SUVR(CGM), r = 0.18 for SUVR(WC)). CONCLUSIONS: There is a high correlation between WM uptake and the measured SUVR due to spill-in effect, and that this effect is reduced when including WM in the reference region. We also evaluated the performance of PVC, and we proposed an analytical correction that can be applied to preprocessed data.