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Record W2077516126 · doi:10.1118/1.4754657

Skin dose in longitudinal and transverse linac‐MRIs using Monte Carlo and realistic 3D MRI field models

2012· article· en· W2077516126 on OpenAlexafffund
Amir Keyvanloo, B Burke, Brad Warkentin, Tony Tadic, S Rathee, Charles Kirkby, D. M. Santos, B. G. Fallone

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsOccupational Cancer Research CentreUniversity of CalgaryUniversity of Alberta
FundersUniversity of AlbertaAlberta Cancer FoundationAlberta InnovatesWestern Canada Research Grid
KeywordsLinear particle acceleratorPhysicsImaging phantomMagnetic fieldMonte Carlo methodMagnetTransverse planePhotonElectronComputational physicsElectromagnetic shieldingField (mathematics)OpticsNuclear magnetic resonanceBeam (structure)Nuclear physicsRadiologyMedicine

Abstract

fetched live from OpenAlex

PURPOSE: The magnetic fields of linac-MR systems modify the path of contaminant electrons in photon beams, which alters patient skin dose. To accurately quantify the magnitude of changes in skin dose, the authors use Monte Carlo calculations that incorporate realistic 3D magnetic field models of longitudinal and transverse linac-MR systems. METHODS: Finite element method (FEM) is used to generate complete 3D magnetic field maps for 0.56 T longitudinal and transverse linac-MR magnet assemblies, as well as for representative 0.5 and 1.0 T Helmholtz MRI systems. EGSnrc simulations implementing these 3D magnetic fields are performed. The geometry for the BEAMnrc simulations incorporates the Varian 600C 6 MV linac, magnet poles, the yoke, and the magnetic shields of the linac-MRIs. Resulting phase-space files are used to calculate the central axis percent depth-doses in a water phantom and 2D skin dose distributions for 70 μm entrance and exit layers using DOSXYZnrc. For comparison, skin doses are also calculated in the absence of magnetic field, and using a 1D magnetic field with an unrealistically large fringe field. The effects of photon field size, air gap (longitudinal configuration), and angle of obliquity (transverse configuration) are also investigated. RESULTS: Realistic modeling of the 3D magnetic fields shows that fringe fields decay rapidly and have a very small magnitude at the linac head. As a result, longitudinal linac-MR systems mostly confine contaminant electrons that are generated in the air gap and have an insignificant effect on electrons produced further upstream. The increase in the skin dose for the longitudinal configuration compared to the zero B-field case varies from ∼1% to ∼14% for air gaps of 5-31 cm, respectively. (All dose changes are reported as a % of D(max).) The increase is also field-size dependent, ranging from ∼3% at 20 × 20 cm(2) to ∼11% at 5 × 5 cm(2). The small changes in skin dose are in contrast to significant increases that are calculated for the unrealistic 1D magnetic field. For the transverse configuration, the entrance skin dose is equal or smaller than that of the zero B-field case for perpendicular beams. For a 10 × 10 cm(2) oblique beam the transverse magnetic field decreases the entry skin dose for oblique angles less than ±20° and increases it by no more than 10% for larger angles up to ±45°. The exit skin dose is increased by 42% for a 10 × 10 cm(2) perpendicular beam, but appreciably drops and approaches the zero B-field case for large oblique angles of incidence. CONCLUSIONS: For longitudinal linac-MR systems only a small increase in the entrance skin dose is predicted, due to the rapid decay of the realistic magnetic fringe fields. For transverse linac-MR systems, changes to the entrance skin dose are small for most scenarios. For the same geometry, on the exit side a fairly large increase is observed for perpendicular beams, but significantly drops for large oblique angles of incidence. The observed effects on skin dose are not expected to limit the application of linac-MR systems in either the longitudinal or transverse configuration.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score0.552

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 head0.025
GPT teacher head0.306
Teacher spread0.281 · 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 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

Citations63
Published2012
Admission routes2
Has abstractyes

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