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Record W2102014790 · doi:10.1109/iembs.2000.901296

Neutron imaging using medical linacs

2002· article· en· W2102014790 on OpenAlexaff
N. Adani, B. G. Fallone

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsUniversity of Alberta
FundersTehran University of Medical Sciences and Health Services
KeywordsNeutronNeutron imagingMaterials scienceDosimetryNeutron radiationNeutron captureNeutron temperatureRadiographyNeutron stimulated emission computed tomographyBonner sphereNuclear physicsPhysicsRadiochemistryNuclear medicineNeutron cross sectionMedicineChemistry

Abstract

fetched live from OpenAlex

The feasibility of imaging soft tissues with neutrons (neutron radiography) from a medical linac as a complement to the conventional X-ray portal imaging is proven. Both direct and indirect neutron radiographs have been successfully obtained. In the direct method, a film is exposed to the beam through a neutron converter. The latter transforms the neutrons into /spl gamma/-rays which subsequently darken the film. In the indirect method, a metallic foil is exposed to the neutrons in the beam and subsequently becomes activated. The resulting gamma rays from the decay of the activation reaction are subsequently used to darken a film and produce an image. In addition, because most of the neutrons generated are fast (average 1 to 2 MeV), a fast neutron radiograph has also been successfully obtained using a CR-39 plate. An exit dosimetry method is suggested for boron neutron capture therapy (BNCT) using neutron radiography. In this manner, the concentration distribution of boron-10 and/or the neutron capture dose in the irradiated region can be obtained through proper calibration. Possible clinical applications of neutron radiography with medical linacs are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.259
Teacher spread0.242 · 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 teacher head, not a consensus.

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

Citations1
Published2002
Admission routes1
Has abstractyes

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