Investigations in x-ray computed tomography polyacrylamide gel dosimetry
Notice bibliographique
Résumé
Polyacrylamide gels (PAGs) are radiosensitive materials currently under development for use as three dimensional (3D) dosimeters in radiation therapy. Dose information is recorded in the gels and extracted through imaging. X-ray computed tomography (CT) has emerged as a promising gel imaging method due to a change in gel density that occurs upon irradiation. The accessibility of CT technology to cancer hospitals makes CT read-out clinically attractive, however the technique remains of limited clinical use due in part to poor dose resolution. This thesis investigates the use of CT for extracting dose information from PAG with an overall goal to improve achievable dose resolution. Thesis results are divided into three studies: a gel compositional study, a study of noise and dose resolution and a digital filtering study. The first study investigates the effects of gel composition on PAG CT dose response and the underlying density change. Systems for irradiating and imaging gels are designed and tested and dose response reproducibility is established. Results indicate dramatic variation in CT dose response sensitivity and range with gel composition. A model is developed to describe gel density change with dose, revealing two fundamental properties of the density to dose response: the density change that occurs per unit polymer yield is highest for gels with low and high concentrations of crosslinking molecule (%C) and the dose response sensitivity is linearly dependent on the total concentration of monomer in the gel. The second study investigates strategies for minimizing noise in x-ray CT polymer gel dosimetry and assesses system performance. Specifically, the effects of phantom design, scanning technique and image voxel size on image noise are investigated. This work leads to the establishment of a method of predicting image noise for any given CT imaging protocol. Image uniformity is also assessed, in the context of noise levels in gel dosimetry. The effect of scanning protocol on imaging time is established and the dose resolution achievable with an optimized system is calculated given voxel size and imaging time constraints. These results, when compared with published values for MRI and optical CT gel dosimetry indicate that CT dose resolution (e.g. 5%, 1 x 1 x 3 mm³ voxels), is still not at the level of the best MRI or optical CT techniques, however fast imaging times makes the rapid acquisition of volumetric data most feasible with x-ray CT. The third study investigates the potential of image filtering for improved dose resolution in CT gel dosimetry. CT image noise is characterized as Gaussian distributed and independent of signal strength and niters for reducing spatially invariant noise are investigated: mean, median, midpoint, adaptive mean, alpha-trimmed mean, sigma mean and a relatively new filter called SUSAN. The filters are tested on a CT image of a PAG irradiated with a clinically relevant dose distribution. Filter performance varies greatly in both achieved dose resolution and affects on the spatial distribution of dose. The ADAPTIVE and SUSAN filters provide the best overall performance, more than halving the dose resolution without significantly distorting the spatial distribution of dose. In summary, this thesis provides new insight into the fundamental nature of PAG density to dose response, develops strategies for minimizing image noise and quantifies system performance and demonstrates that digital image filtering is an effective tool to provide additional improvements to dose resolution.
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 | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,001 | 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 ».