Analytically based photon scatter modeling for a multipinhole cardiac SPECT camera
Notice bibliographique
Résumé
PURPOSE: Dedicated cardiac SPECT scanners have improved performance over standard gamma cameras allowing reductions in acquisition times and/or injected activity. One approach to improving performance has been to use pinhole collimators, but this can cause position-dependent variations in attenuation, sensitivity, and spatial resolution. CT attenuation correction (AC) and an accurate system model can compensate for many of these effects; however, scatter correction (SC) remains an outstanding issue. In addition, in cameras using cadmium-zinc-telluride-based detectors, a large portion of unscattered photons is detected with reduced energy (low-energy tail). Consequently, application of energy-based SC approaches in these cameras leads to a higher increase in noise than with standard cameras due to the subtraction of true counts detected in the low-energy tail. Model-based approaches with parallel-hole collimator systems accurately calculate scatter based on the physics of photon interactions in the patient and camera and generate lower-noise estimates of scatter than energy-based SC. In this study, the accuracy of a model-based SC method was assessed using physical phantom studies on the GE-Discovery NM530c and its performance was compared to a dual energy window (DEW)-SC method. METHODS: Tc-SPECT (140 ± 14 keV) were validated with point-source measurements and 15 anthropomorphic cardiac-torso phantom experiments and varying levels of extra-cardiac activity causing scatter inside the heart. The activity inserted into the myocardial compartment of the phantom was first measured using a dose calibrator. CT images were acquired on an Infinia Hawkeye (GE Healthcare) SPECT/CT and coregistered with emission data for AC. For comparison, DEW scatter projections (120 ± 6 keV ) were also extracted from the acquired list-mode SPECT data. Either APD or DEW scatter projections were subtracted from corresponding 140 keV measured projections and then reconstructed with AC (APD-SC and DEW-SC). Quantitative accuracy of the activity measured in the heart for the APD-SC and DEW-SC images was assessed against dose calibrator measurements. The difference between modeled and acquired projections was measured as the root-mean-squared-error (RMSE). APD-modeled projections for a clinical cardiac study were also evaluated. RESULTS: APD-modeled projections showed good agreement with SPECT measurements and had reduced noise compared to DEW scatter estimates. APD-SC reduced mean error in activity measurement compared to DEW-SC in images and the reduction was statistically significant where the scatter fraction (SF) was large (mean SF = 28.5%, T-test p = 0.007). APD-SC reduced measurement uncertainties as well; however, the difference was not found to be statistically significant (F-test p > 0.5). RMSE comparisons showed that elevated levels of scatter did not significantly contribute to a change in RMSE (p > 0.2). CONCLUSIONS: Model-based APD scatter estimation is feasible for dedicated cardiac SPECT scanners with pinhole collimators. APD-SC images performed better than DEW-SC images and improved the accuracy of activity measurement in high-scatter scenarios.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».