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Fluence profiles and energy spectral distributions of 100, 110, and 125 kVp photon beams: results of Monte Carlo simulations for a Varian OBI 1.4 CBCT

2019· article· en· W2944612838 on OpenAlexfundno aff
Heri Setiawan, Rena Widita

Bibliographic record

VenueJournal of Physics Conference Series · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsnot available
FundersNational Research Council CanadaInstitut Teknologi Bandung
KeywordsMonte Carlo methodFluenceImage-guided radiation therapyPhotonPhysicsBeam (structure)Radiation treatment planningCone beam computed tomographyEnergy (signal processing)OpticsMedical physicsComputational physicsComputer scienceMedical imagingRadiation therapyMedicineComputed tomographyStatisticsMathematicsRadiologyArtificial intelligence

Abstract

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Cone Beam Computed Tomography (CBCT) imaging for daily patient localization has gained enormous popularity as one of Image Guided Radiation Therapy (IGRT) methods in recent years. This is largerly due to the need of higher precision and accuracy in conformal beam delivery technique which is known as Intensity Modulated Radiation Therapy (IMRT). The success of this IMRT method is mainly determined by the treatment planning systems. The aim of this research is to provide detailed characteristics of incident photon beams for different beam energies from a Varian OBI 1.4 CBCT. The detailed characteristics consists of energy spectral distributions and fluence profiles. This information is critical to the future development of accurate treatment planning systems. BEAMnrc as one of EGSnrc Monte Carlo user code, has been used to simulate 100, 110, and 125 kVp photon beams from x-ray tube of a Varian OBI 1.4 CBCT. The details of each particle's complete history including where it has been and where it has interacted is stored in a phase space (phsp) data file. The phsp files are analyzed to obtain fluence profiles and energy spectral distributions.

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 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: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.436

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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.

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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