Elastic motion correction improves Rb-82 Cardiac PET ECG-gated image quality
Notice bibliographique
Résumé
23 Objectives: ECG-gated imaging for evaluation of left ventricular (LV) contractile function is an integral part of myocardial perfusion imaging (MPI) but can be challenging with Rb-82 due to the short half-life and low count-statistics. Motion-compensated image reconstruction was reported recently on some latest-generation PET-CT scanners, but has not been optimized for Rb-82 PET MPI. This study evaluated the effect of elastic motion-compensated (MOCO) image reconstruction on ECG-gated Rb-82 PET image quality. Methods: ECG-gated and ungated (static) images were analyzed at rest and stress from N=20 sequential patients referred for Rb-82 MPI (9 MBq/kg) on a PET-CT scanner with ≍200 ps time-of-flight (TOF) resolution. Standard (no-MOCO) ECG-gated images were reconstructed using 6 mm Gaussian filter, and used to estimate contractile and respiratory motion vector fields (MVF). Additional ECG-gated images were then reconstructed using the contractile-MVF (single-MOCO) and combined respiratory- and contractile-MVF (dual-MOCO) information integrated into the iterative reconstruction algorithm (OSEM with 4 iterations and 5 subsets). Static (ungated) images at rest were also reconstructed using 2, 4, 6 mm Gaussian filters for comparison of image quality. Myocardium signal recovery was measured as the maximum activity in the left ventricle (LV) at end-diastole (ED). Background signal and noise were measured as the left atrium blood cavity mean and standard deviation, also at the ED phase. LV myocardium signal-to-noise ratio (SNR) and myocardium-to-blood contrast-to-noise ratio (CNR) values were calculated for the static and ECG-gated images. SNR and CNR were compared between reconstruction methods using paired t-tests. Results: End-diastolic image SNR and CNR increased in 95% (or 55%) of patients at rest using single-MOCO (or dual-MOCO) compared to standard ECG-gated reconstruction. Similarly at stress, SNR and CNR increased in 100% (or 60%) of patients using single-MOCO (or dual-MOCO) reconstruction. Single-MOCO reconstruction significantly improved SNR (+48%) and CNR (+51%), both at stress (+43%) and rest (+56%) (all P P > 0.01), with 40% of scans resulting in lower image quality compared to the standard (no-MOCO) gated reconstruction. The dual-MOCO gated images reconstructed with 6mm filter had image SNR and CNR that were similar to static ungated image reconstructed with 2 mm filtering, whereas the single-MOCO gated images were similar to the static images with 4 mm filtering. Both single- and dual-MOCO images had lower SNR and CNR compared to the static images reconstructed with the same 6 mm filter, suggesting that there was some residual noise in the single-MOCO estimated MVF that could be further improved. Image quality (SNR and CNR) decreased with patient weight (both P < 0.05), likely due to increased attenuation effects despite the use of proportional weight-based dosing, suggesting that larger patients require even higher administered activity to achieve uniform image quality. Conclusions: Single (contractile) motion-compensated image reconstruction improved the end-diastolic image SNR and CNR over standard uncompensated or dual motion-compensated image reconstruction, and is recommended for optimal ECG-gated image quality using Rb-82 on a current-generation TOF PET-CT scanner.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».