Determining the effects of microsphere and surrounding material composition on <sup>90</sup>Y dose kernels using <scp>egsnrc</scp> and <scp>mcnp5</scp>
Notice bibliographique
Résumé
PURPOSE: Recent advances in the imaging of (90)Y using positron emission tomography (PET) and improved uncertainty in the branching ratio for the internal pair production component of (90)Y decay allow for a more accurate determination of the activity distribution of (90)Y microspheres within a patient. This improved activity distribution can be convolved with the dose kernel of (90)Y to calculate the dose distribution within a patient. This work investigates the effects of microsphere and surrounding material composition on (90)Y dose kernels using egsnrc and mcnp5 and compares the results of these two transport codes. METHODS: Monte Carlo simulations were performed with egsnrc and mcnp5 to calculate the dose rate at multiple radial distances around various (90)Y sources. Point source simulations were completed with mcnp5 to determine the optimal electron transport settings for this work. After determining the optimal settings, point source simulations were completed using egsnrc (user code edknrc) and mcnp5 in water and liver [as defined by the International Commission on Radiation Units and Measurements (ICRU) Report 44]. The results were compared to ICRU Report 72 reference data. Point source simulations were also completed in water with a density of 1.06 g[middle dot]cm(-3) to evaluate the effect of the density of the surrounding material. Glass and resin microsphere simulations were performed with average and maximum diameter and density values (based on values given in the literature) in water and in liver. The results were compared to point source simulation results using the same transport code and in the same surrounding material. All simulations had statistical uncertainties less than 1%. RESULTS: The optimal transport settings in mcnp5 for this work included using the energy-and step-specific algorithm (DBCN 17J 2) and ESTEP set to 10. These settings were used for all subsequent simulations with mcnp5. The point source simulations in water for both egsnrc and mcnp5 were found to agree within 2% of the ICRU 72 reference data over the investigated range. Point source simulations in liver had large differences relative to ICRU 72, approaching -60% near the maximum range of (90)Y. These differences are mostly attributed to the difference in density between water (1.0 g[middle dot]cm(-3)) and liver (1.06 g[middle dot]cm(-3)). Glass and resin microsphere simulations showed a slight decrease in the dose rate near the maximum range of (90)Y relative to the point source simulations. The largest relative differences were approximately -4.2% and -2.8% for the glass and resin microspheres, respectively. Agreement between the egsnrc and mcnp5 simulations results was generally good. CONCLUSIONS: The presence of the microsphere material causes slight differences in the (90)Y dose kernel compared to those calculated with point sources. Large differences were seen between simulations in water and those in liver. For the most accurate calculation of the dose distribution, the density of the patient's liver should be accounted for in the calculation of the dose kernel. Lastly, due to the need to determine the optimal transport settings with mcnp5, electron transport with this code should be used with caution.
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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 | 0,002 |
| 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 | 0,000 | 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) | 0,001 | 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 ».