Layered mass geometry: a novel technique to overlay seeds and applicators onto patient geometry in Geant4 brachytherapy simulations
Notice bibliographique
Résumé
A problem faced by all Monte Carlo (MC) particle transport codes is how to handle overlapping geometries. The Geant4 MC toolkit allows the user to create parallel geometries within a single application. In Geant4 the standard mass-containing geometry is defined in a simulation volume called the World Volume. Separate parallel geometries can be defined in parallel worlds, that is, alternate three dimensional simulation volumes that share the same coordinate system with the World Volume for geometrical event biasing, scoring of radiation interactions, and/or the creation of hits in detailed readout structures. Until recently, only one of those worlds could contain mass so these parallel worlds provided no solution to simplify a complex geometric overlay issue in brachytherapy, namely the overlap of radiation sources and applicators with a CT based patient geometry. The standard method to handle seed and applicator overlay in MC requires removing CT voxels whose boundaries would intersect sources, placing the sources into the resulting void and then backfilling the remaining space of the void with a relevant material. The backfilling process may degrade the accuracy of patient representation, and the geometrical complexity of the technique precludes using fast and memory-efficient coding techniques that have been developed for regular voxel geometries. The patient must be represented by the less memory and CPU-efficient Geant4 voxel placement technique, G4PVPlacement, rather than the more efficient G4NestedParameterization (G4NestedParam). We introduce for the first time a Geant4 feature developed to solve this issue: Layered Mass Geometry (LMG) whereby both the standard (CT based patient geometry) and the parallel world (seeds and applicators) may now have mass. For any area where mass is present in the parallel world, the parallel mass is used. Elsewhere, the mass of the standard world is used. With LMG the user no longer needs to remove patient CT voxels that would include for example seeds. The patient representation can be a regular voxel grid, conducive to G4NestedParam, and the patient CT derived materials remain exact, avoiding the inaccuracy of the backfilling technique. Post-implant dosimetry for one patient with (125)I permanent seed implant was performed using Geant4 version 9.5.p01 using three different geometrical techniques. The first technique was the standard described above (G4PVPlacement). The second technique placed patient voxels as before, but placed seeds with LMG (G4PVPlacement+LMG). The third technique placed patient voxels through G4NestedParam and seeds through LMG (G4NestedParam+LMG). All the scenarios were calculated with 3 different image compression factors to manipulate the number of voxels. Additionally, the dosimetric impact of the backfilling technique was investigated for the case of calcifications in close proximity of sources. LMG eliminated the need for backfilling and simplified geometry description. Of the two LMG techniques, G4PVPlacement+LMG had no benefit to calculation time or memory use, actually increasing calculation time, but G4NestedParam+LMG reduced both calculation time and memory. The benefits of G4NestedParam+LMG over standard G4PVPlacement increased with increasing voxel numbers. For the case of calcifications in close proximity to sources, LMG not only increased efficiency but also yielded more accurate dose calculation than G4PVPlacement. G4NestedParam in combination with LMG present a new, efficient approach to simulate radiation sources that overlap patient geometry. Cases with brachytherapy applicators would constitute a direct extension of the method.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,000 | 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 tête enseignante, pas un consensus.
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