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Record W2169364006 · doi:10.1088/0031-9155/55/4/006

Radio frequency shielding for a linac-MRI system

2010· article· en· W2169364006 on OpenAlexafffund
M Lamey, B Burke, E. Blosser, S Rathee, Nicola De Zanche, B. G. Fallone

Bibliographic record

VenuePhysics in Medicine and Biology · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of Alberta
FundersNational Research Council CanadaAlberta Cancer Foundation
KeywordsLinear particle acceleratorElectromagnetic shieldingRadio frequencyRF power amplifierNoise (video)OpticsPhysicsNuclear magnetic resonanceComputer scienceTelecommunicationsBeam (structure)OptoelectronicsAmplifier

Abstract

fetched live from OpenAlex

The real-time operation of a linac-MRI system will require proper radio frequency (RF) shielding such that the MRI images can be acquired without extraneous RF noise from the linac. We report on the steps taken to successfully shield the linac from the MRI such that the two devices can operate independently of one another. RF power density levels are reported internally and externally to the RF cage which houses the linac and MRI. The shielding effectiveness of the RF cage has been measured in the frequency range 1-50 MHz and is presented. Lastly MRI images of two phantoms are presented during linac operation. This work illustrates that the accelerating structure of a linac and an MRI can be housed within the same RF cage. The 6 MV linac can be operated to produce radiation with no measurable degradation in image quality due to RF effects.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.003

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 head0.078
GPT teacher head0.387
Teacher spread0.309 · 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

Citations20
Published2010
Admission routes2
Has abstractyes

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