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Modelling PET radionuclide production in tissue and external targets using Geant4

2017· article· en· W4243313925 on OpenAlexafffund
T. Amin, Angelo Infantino, C. Lindsay, R. J. Barlow, Cornelia Hoehr

Bibliographic record

VenueJournal of Physics Conference Series · 2017
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsTRIUMF
FundersUniversity of British ColumbiaTRIUMF
KeywordsCyclotronRadionuclideProtonProton therapyIsotopeRadiochemistryRange (aeronautics)Nuclear physicsPositronNuclear medicinePositron emissionPositron emission tomographyPhysicsMaterials scienceChemistryMedicine

Abstract

fetched live from OpenAlex

The Proton Therapy Facility in TRIUMF provides 74 MeV protons extracted from a 500 MeV H - cyclotron for ocular melanoma treatments. During treatment, positron emitting radionuclides such as 1 C, 15 O and 13 N are produced in patient tissue. Using PET scanners, the isotopic activity distribution can be measured for in-vivo range verification. A second cyclotron, the TR13, provides 13 MeV protons onto liquid targets for the production of PET radionuclides such as 18 F, 13 N or 68 Ga, for medical applications. The aim of this work was to validate Geant4 against FLUKA and experimental measurements for production of the above-mentioned isotopes using the two cyclotrons. The results show variable degrees of agreement. For proton therapy, the proton-range agreement was within 2 mm for 11 C activity, whereas 13 N disagreed. For liquid targets at the TR13 the average absolute deviation ratio between FLUKA and experiment was 1.9±2.7, whereas the average absolute deviation ratio between Geant4 and experiment was 0. 6±0.4. This is due to the uncertainties present in experimentally determined reaction cross sections.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.054
GPT teacher head0.317
Teacher spread0.263 · 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".

Quick stats

Citations3
Published2017
Admission routes2
Has abstractyes

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