Voxel-wise estimation of nondisplaceable binding (VND) for PET SV2A synaptic density imaging with 11C-UCB-J
Notice bibliographique
Résumé
578 Objectives: For noninvasive quantification of PET images by reference region methods, a region in which the total radioligand uptake is representative of the nondisplaceable (i.e. free + non-specifically bound) volume of distribution (VND) of the radiotracer is used to determine the specific binding of the PET radiotracer to its target, such as the binding potential with respect to the nondisplaceable compartment BPND. VND can be determined in vivo using pairs of baseline and blocking scans using specific pharmaceuticals that compete with the PET tracer at its target. Typically, the occupancy (r) and VND can be estimated from a Lassen Occupancy Plot, in which the difference of volume of distribution VT between baseline and blocking conditions is plotted against the baseline VT. The slope of the best fit line of this data is the estimated occupancy, and the x-intercept represents VND. This analysis requires the assumption that both the occupancy and the nondisplaceable binding are uniform across all brain regions. The objective of this work is to examine the assumption of uniform VND by using a population of baseline and blocking scans to estimate voxel-wise estimates of VND and the specific volume of distribution (VS) using 11C-UCB-J, a PET radioligand that targets the synaptic vesicle glycoprotein-2A (SV2A). Methods: Nine healthy subjects underwent a PET scan with 11C-UCB-J before and after a single dose of the SV2A-specific drug Brivaracetam (BRV). Images were acquired on the High-Resolution Research Tomograph (HRRT) and were reconstructed with OSEM (2 iterations, 30 subsets). Time-activity data and arterial input functions were analyzed using a 1-tissue compartment model. Parametric VT images were created for both baseline and blocking scans and were registered to the Montreal Neurological Institute (MNI) template space. Blood samples were taken at the beginning, middle, and end of the blocking scan to determine drug plasma levels. At each voxel i, using data from baseline and blocking VT images for each subject j, ordinary least squares methods were used to fit the model in Eq. 1 VT,i,j = VND,i + (1-rj) [asterisk] VS,i (Eq. 1) where rj=cj/(cj+IC50) at blocking, where cj is the plasma BRV concentration during each scan (0 for baseline scans) and IC50 is the known BRV concentration at which 50% of SV2A occupancy is reached. Parametric images were created for VND and VS. VND estimates in brain regions were compared to the average extrapolated VND found from the corresponding Lassen Occupancy plots. Results: Across the nine subjects, the average (± SD) Lassen VND was 2.65 (± 0.57) mL/cm3. Using the voxel-wise estimation of VND and VS, the estimated VND was nonuniform within the brain, ranging from an average of 2.81 (± 0.24) ml/cm3 in the gray matter to 3.38 mL/cm3 in the white matter centrum semiovale region (Table 1). On average, the voxel-wise VND estimates in the gray matter regions were consistent with the Lassen VND estimates. VS was also nonuniform, ranging from 1.46 mL/cm3 in the centrum semiovale to an average of 18.2 (± 1.7) mL/cm3 in the gray matter. Conclusions: The current work proposes a voxel-wise method for estimating the VND and VS of SV2A PET radiotracer, 11C-UCB-J using pairs of baseline and blocking scans. With this method, we report variable VND estimates across the brain, which ranged from -6 to +24% different from the average VND from the Lassen Occupancy plot. A non-zero VS estimate in the centrum semiovale, a proposed reference region for 11C-UCB-J, was also observed. Though non-zero VS in the white matter is not ideal, it is a small fraction of what is observed in the gray matter regions (~8%), and may still be useful for reference region quantification methods. The proposed voxel-wise estimation method may also be useful for other PET radiotracers, to validate or explore new reference regions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».