1. Volume-of-interest-based supervised cluster analysis for pseudo-reference region selection in [18F]DPA-714 PET imaging of the rat brain
- Author
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Igor Fagner Vieira, Dieter Ory, Cindy Casteels, Fernando R. A. Lima, Koen Van Laere, Guy Bormans, and Michel Koole
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Supervised clustering analysis ,Pseudo-reference brain region ,[18F]DPA-714 PET ,Rat brain ,Neuroinflammation ,Medical physics. Medical radiology. Nuclear medicine ,R895-920 - Abstract
Abstract Method Aim of this study was to automatically select a suitable pseudo-reference brain region for the accurate, non-invasive quantification of neuroinflammation in a rat brain using dynamic [18F]DPA-714 PET imaging. Procedures A supervised clustering analysis approach considering three kinetic classes (SVCA3) was used to select an appropriate pseudo-reference brain region. This pseudo-reference region was determined by selecting only brain regions with low specific tracer uptake (SVCA3low) or by taking into account all brain regions and weighting each brain region with the corresponding fraction of low specific binding (SVCA3wlow). Both SVCA3 approaches were evaluated in an animal model of neuro-inflammation induced by lipopolysaccharide injection in the right striatum of female Wistar rats. For this study setup, a population of 25 female Wistar rats received a dynamic PET scan after injection of ~ 60 MBq [18F]DPA-714. Animals were scanned at baseline (n = 3) and at different time points after inducing neuroinflammation: 1 day (n = 3), 3 days (n = 12), 7 days (n = 4), and 30 days (n = 3). Binding potential (BP) values using a simplified reference tissue model (SRTM) and the contralateral striatum as pseudo-reference region were considered as a reference method (BPL STR) and compared with SRTM BP values using a pseudo-reference region obtained by either the SVCA3low or SVCA3wlow approach for both a 90- and 120-min acquisition time interval. Results For the right striatum, SRTM BP values using a SVCA3low- or SVCA3wlow-based pseudo-reference region demonstrated a strong and highly significant correlation with SRTM BPL STR values (Spearman r ≥ 0.89, p
- Published
- 2018
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