Automated beam model optimization
Notice bibliographique
Résumé
PURPOSE: The beam model in a three dimensional treatment planning system (TPS) defines virtually the mechanical and dosimetric characteristics of a treatment unit. The manual optimization of a beam model during commissioning can be a time consuming task due to its iterative nature. Furthermore, the quality of the beam model commissioning depends on the user's ability to manage multiple parameters and assess their impact on the agreement between measured and calculated dose. The objective of this work is to develop and validate the performance of an automated beam model optimization system (ABMOS) based on intensity modulated radiotherapy (IMRT) beam measurements to improve beam model accuracy while streamlining the commissioning process. METHODS: The ABMOS was developed to adjust selected TPS beam model parameters iteratively to maximize the agreement between measured and calculated 2D dose maps obtained for an IMRT beam pattern. A 2D diode array with high spatial resolution detectors was used to sample the entire IMRT beam pattern in a single dose measurement. The use of an IMRT beam pattern with large number of monitor units was selected to highlight the difference between planned and delivered dose and improve the signal to noise ratio in the low dose regions. ABMOS was applied to the optimization of a beam model for an Elekta Synergy S treatment unit. The optimized beam model was validated for two anatomical sites (25 paraspinal and 25 prostate cases) using two independent patient-specific IMRT quality control (QC) methods based on ion chamber and 2D diode array measurements, respectively. The conventional approach of comparing calculated and measured beam profiles and percent-depth dose curves was also used to assess improvement in beam model after ABMOS optimization. Elements of statistical process control were applied to the process of patient-specific QC performed with the ion chamber and the 2D array to complement the model comparison. RESULTS: After beam model optimization with ABMOS, improvement in planned to delivered dose agreement was demonstrated with both patient-specific IMRT QC methods and the calculated to measured profile comparison. In terms of ion chamber measurements, the largest improvement was observed for the paraspinal cases with the mean measured to calculated dose difference at the low dose points decreasing from - 13.8% to 2.0% with the optimized beam model. The 2D diode array patient-specific QC also demonstrated clearly the improvement in beam model for both paraspinal and prostate cases with, on average, more than 96% of the diodes satisfying tolerances of 3% of dose difference or 2 mm of distance to agreement after ABMOS optimization. The capability index (C(pk)) for both patient-specific QC methods also increased with the optimized beam model. CONCLUSIONS: In this work, ABMOS was developed to use 2D diode array measurements of an IMRT beam pattern for the automated multivariable optimization of a TPS beam model. Based on the observed improvements in patient-specific QC results for 25 paraspinal and 25 prostate plans, optimization of the remaining clinical beam models using ABMOS is now ongoing in the institution.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».