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Record W7162011887 · doi:10.82308/42972

Secondary neutrons around clinical electron and proton beams

2018· dissertation· en· W7162011887 on OpenAlexaboutno aff
G. Al Makdessi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsnot available
Fundersnot available
KeywordsNeutronProtonBonner sphereElectronNeutron detectionSpectral lineNeutron temperatureNeutron sourceBeam (structure)

Abstract

fetched live from OpenAlex

As is the case for high-energy photon beam therapy (> 10 MV), secondary neutrons are produced during electron and proton therapy. These neutrons, which extend in energy up to the maximum energy of the primary beam, result in a whole-body radiation dose to the patient that presents a risk for a second, radiation-induced, cancer. To account for the second-cancer risk, it is necessary to understand the spectra of the neutrons involved.In this research project, the spectra of secondary neutrons arising from clinical electron and proton beams were measured. The neutron spectra around electron beams were measured for a Varian linear accelerator with maximum electron energy of 20 MeV. The neutron spectra around proton beams were measured in a treatment room with a gantry and a dedicated nozzle at the Skandion proton therapy clinic in Uppsala, Sweden. The proton beams were generated by the compact IBA Proteus Plus cyclotron, which has a maximum proton energy of 230 MeV.Measurements were performed using the Nested Neutron Spectrometer (NNS™, Detec Inc, Gatineau Quebec). The NNS™ comprises a central He-3 detector (active neutron detection) with seven high density polyethylene shells that may be arranged around the detector in Russian doll fashion. The neutron spectra arising from proton beams were measured using a modified version of the NNS™ incorporating a brass moderator shell to provide an extended energy range (EER) for the detection of high-energy neutrons. The raw neutron data were unfolded using the Maximum a Posteriori (MAP) method (custom-written in C++) and the vendor-supplied moderator response functions for the NNS™. Note that the MAP is a modified version of the Maximum Likelihood Estimation Method (MLEM) that penalizes any high noise component. To plot the secondary neutron fluence as a function of energy, the ROOT data analysis library from CERN was used. Additionally, the ICRP conversion coefficients were used to calculate the neutron ambient dose equivalent rates around the electron and proton beams.The effect of several parameters on the neutron fluence rate and on the neutron dose equivalent rate were studied for each primary beam situation. For the electron beams, it was observed that as the energy of the primary electron beam increases, the neutron fluence rate and dose equivalent rate increase. In addition, the presence of a solid water phantom in the primary electron beam does not affect the neutron spectra. However, when closing the jaws in the treatment head, the neutron fluence rate and dose equivalent rate increase significantly which means that the main production of secondary neutrons around electron beams lies in the treatment head of the linear accelerator.For the proton beams, it was observed that as the energy of the primary proton beam increases, the neutron fluence rate and dose equivalent rate increase. The presence of a water phantom in the primary beam significantly enhances the direct and evaporation peaks of the neutron spectra which indicates that an important production of secondary neutrons around proton beams happens in the patient body. The evaporation and direct peaks of the neutron spectrum dominate the thermal peak when measuring on the couch (close to the neutron source), while the thermal peak dominates when measuring in the maze-room junction (far from the neutron source).

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.366
Teacher spread0.349 · 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 designSimulation or modeling
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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