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Record W7055801806

On the detector response and the reconstruction of the source intensity distribution in small photon fields

2016· dissertation· en· W7055801806 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2016
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsMcGill University
FundersAlexander S. Onassis Public Benefit FoundationMcGill University Health CentreNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsDetectorIntensity (physics)Distribution (mathematics)PhotonField (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

On the detector response and the reconstruction of the source intensity distribution in small photon fields by Pavlos PAPACONSTADOPOULOS iii This PhD thesis pertains to the subject of small field dosimetry.Small photon fields, generally referring to fields smaller than 2 × 2 cm 2 , are routinely used in the clinic during modern radiotherapy treatment deliveries.However, several investigators have raised questions regarding the accuracy of the dosimetry in such conditions.An important issue involves the significant perturbations that are caused by most detectors in small field conditions.These perturbations will result in erroneous dose calculations during treatment planning and affect the treatment outcome.Currently it is still not clear which detector system should be preferred in small fields.In this work, the response of modern dosimeters is investigated via Monte Carlo simulations and experiments.Detector-specific correction and perturbation factors are reported for modern detector systems and their accuracy is evaluated experimentally.The need for off-axis corrections is also investigated, a less explored area in previous research.The calibration process, essential for some detector systems, is carefully examined and recommendations are provided.Another unexplored area that directly affects the dosimetry in small fields is the accurate knowledge of the X-ray source distribution.As the field size is reduced and becomes comparable to the X-ray source size, significant accelerator output variations are observed.In principle these variations can be predicted if direct source reconstruction methods are developed.Current source reconstruction methods require special equipment, not easily accessible in every clinic.In this work we suggest a novel approach for the source reconstruction problem, which requires no prior knowledge of the source's functional form nor any specialized equipment.The method is based on a maximum-likelihood expectation-maximization algorithm iv and utilizes small field photon fluence profiles.The method is evaluated and benchmarked against simulations and experiments and significant sources of uncertainties are identified.v Cette thèse de doctorat porte sur le sujet de la dosimétrie en petits champs.Les sources de photons dits en "petits champs " font généralement référence à des champs plus petits que 2 × 2 cm 2 et sont couramment utilisés en clinique durant les traitement de radiothérapie modernes.Cependant, plusieurs chercheurs ont soulevé des questions quant à l'exactitude de la dosimétrie dans de telles conditions.Une question importante concerne les perturbations considérables causées par la plupart des détecteurs dans des conditions utilisant des petits champs.Ces perturbations vont entraîner des calculs de doses erronés lors de la planification du traitement et ainsi affecter leur résultat.Actuellement, le système de détecteur à privilégier dans des conditions de petits champs n'a pas encore été déterminé.Dans ce travail, la réponse des dosimètres modernes est étudiée via des simulations Monte-Carlo, ainsi que dans des conditions expérimentales.Les facteurs de corrections et de perturbations spécifiques aux différents détecteurs modernes sont présentés dans cette étude, et leur exactitude est évaluée expérimentalement.La nécessité des corrections hors-axe est également étudiée, une zone moins explorée dans les précédents travaux de recherches.Le processus d'étalonnage, essentiel pour certains systèmes de détection, est méticuleusement examinée et des recommandations sont établies.Un autre domaine inexploré qui affecte directement la dosimétrie en petits champs est la connaissance précise de la distribution spatiale de la source de rayons X. Lorsque la taille du champ est considérablement réduite et devient comparable à la taille de la source, d'importantes variations en sortie de l'accélérateur linéaire sont observées.En principe, ces variations peuvent être prédites si des méthodes de reconstruction directe de la source vi à rayons X sont développés.Les méthodes de reconstruction de la source actuellement utilisées nécessitent des équipements spéciaux qui sont difficilement accessibles dans chaque clinique de radiothérapie.Dans cette étude, nous proposons une nouvelle approche pour résoudre le problème de la reconstruction de la source à rayons X qui ne nécessite aucune connaissance préalable de la forme fonctionnelle de la source, ni aucun équipement spécialisé.La méthode est basée sur un algorithme dénommé "maximum-likelihood expectation-maximization" et utilise des profils de fluence de photons en petits champs.La méthode est évaluée et comparée à des simulations et expériences de références, et d'importantes sources d'incertitudes sont identifiées.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.218
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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