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Record W2528090820 · doi:10.1118/1.4963803

Influence of standard RF coil materials on surface and buildup dose from a 6 MV photon beam in magnetic field

2016· article· en· W2528090820 on OpenAlexafffund
Andrei Ghila, B. G. Fallone, S Rathee

Bibliographic record

VenueMedical Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMaterials scienceElectromagnetic coilBeam (structure)ElectromagnetMagnetOpticsLinear particle acceleratorRadiofrequency coilMagnetic fieldNuclear magnetic resonancePhysics

Abstract

fetched live from OpenAlex

PURPOSE: Magnetic resonance guided teletherapy systems aspire to image the patient concurrently with the radiation delivery. Thus, the radiofrequency (RF) coils used for magnetic resonance imaging, placed on or close to patient skin and in close proximity to the treatment volume, would be irradiated leading to modifications of radiation dose to the skin and in the buildup region. The purpose of this work is to measure and assess these dose modifications due to standard off-the-shelf RF coil materials. METHODS: A typical surface coil was approximated as layered sheets of polycarbonate, copper tape, and Teflon to emulate the base, conductor, and cover, respectively. A separate investigation used additional coil materials, such as copper pipe, plastic coil housing, a typical coil padding material, and a thin copper conductor. The materials were placed in the path of a 6 MV photon beam at various distances from polystyrene phantoms in which the surface and buildup doses were measured. The experiments were performed on a clinical Varian linac with no magnetic field and with a 0.21 T electromagnet producing a magnetic field parallel to the beam central axis. The authors repeated similar experiments in the presence of a 0.22 T magnetic field oriented perpendicular to the beam central axis using an earlier linac-MR prototype, with a biplanar permanent magnet. The radiation detectors used for the measurements were two different parallel plate ion chambers and GAFChromic films. RESULTS: ) was increased to 63% by the coil padding material and to >74% by all other materials when placed in direct contact with the phantom, irrespective of magnetic field presence or orientation. Without a magnetic field, the surface dose decreased as the test materials were moved away from the phantom surface toward the radiation source, reaching between 30% and 40% at 10 cm gap between the phantom and the test materials. In the presence of the transverse magnetic field, the surface dose reduction was more rapid reaching a dose level of 30%-40% with only 3-4 cm gap. In the presence of the parallel magnetic field, as expected, the surface dose did not decrease considerably as the gap between the phantom surface and test materials was increased; the surface dose remained >60% at 10 cm gap for all tested materials except for the thin copper conductor. CONCLUSIONS: As expected, placing coil materials in direct contact with the phantom surface increases the surface dose considerably. The surface dose is reduced by creating a gap between the coil materials and phantom surface. This dose reduction happens more rapidly in the presence of a transverse magnetic field. However, the surface dose stays relatively large irrespective of the gap in the presence of a parallel magnetic field. Thus, the standard, off-the-shelf RF coils should be used with caution in integrated linac-MR systems, especially those using a parallel magnetic field orientation in which case the RF coils will probably need to be reconfigured to create open ports for the radiation beam.

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 score0.226
Threshold uncertainty score0.544

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)0.0000.000

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 head0.005
GPT teacher head0.263
Teacher spread0.258 · 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".

Quick stats

Citations20
Published2016
Admission routes2
Has abstractyes

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