Model Based Scatter Calculations for A Dedicated Cardiac SPECT Camera
Notice bibliographique
Résumé
123 Objectives In CZT-based dedicated cardiac SPECT cameras, energy-window-based scatter estimation is more difficult due to the detection of a large fraction of unscattered photons with reduced energy (“low-energy tail”). Consequently the unscattered photon signal in the scatter window data leads to an increase in noise in the scatter corrected projections. Model-based methods of scatter estimation have less noise and have been shown to be more accurate for cameras with parallel-hole collimators. Thus, model-based approaches may be advantageous for dedicated cardiac systems, but these methods are more complicated in multi-pinhole cameras due to the small field-of-view and distance-dependent variations in sensitivity and magnification. In this study, accuracy of a model based SC method was assessed for a multi-pinhole cardiac SPECT camera using physical phantom studies in comparison to a dual energy window (DEW) SC method. Methods The analytical photon distribution (APD) method was implemented. This method calculates the distribution of probabilities that photons emitted inside the body will scatter in the surrounding scattering medium and be subsequently detected. Scatter calculations were validated by 15 99mTc-SPECT phantom experiments using an anthropomorphic torso phantom with a cardiac insert, in which activity ratios were selected to resemble a clinical scan. Varying levels of photon scatter inside the myocardial compartment was implemented by increasing the activity concentration in the soft tissue compartment of the phantom. The activity inserted into the myocardial compartment of the phantom was first measured using a dose calibrator. SPECT images (140 +/- 14 keV) were acquired on a Discovery NM530c (GE Healthcare) cardiac camera. CT images were acquired on a Infinia-Hawkeye (GE Healthcare) SPECT/CT and co-registered with emission data for AC. MLEM image reconstruction was performed off-line. APD-scatter projections were generated using the reconstructed images and attenuation maps. For comparison, DEW scatter projections (120 +/- 6 keV) were also extracted from the acquired listmode 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, respectively). Activity in the heart was recovered using heart masks generated based on a CT based binary template of the myocardial compartment. of the difference in the total cardiac activity from the dose calibrator measurement was compared between APD-SC and DEW-SC images. 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. While APD-SC reduced mean error in activity measurement compared to DEW-SC in images, T-tests showed the reduction to be statistically significant only where the scatter fraction (SF) was large (mean SF = 28.5%, 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). Comparison of modeled and acquired projections from a clinical study showed good agreement. Conclusions An APD model-based scatter estimation method produces projections that agree well with data acquired on a dedicated cardiac SPECT scanner with pinhole collimators for both phantom and clinical studies. APD-SC images have lower noise than DEW-SC images and provided a more accurate measure of cardiac activity 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) | 0,000 | 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 | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».