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Record W3016735578 · doi:10.1002/mp.14185

Measurement of surface dose in an MR‐Linac with optically stimulated luminescence dosimeters for IMRT beam geometries

2020· article· en· W3016735578 on OpenAlexaff
Stephanie Lim‐Reinders, Brian Keller, Arjun Sahgal, Brige Chugh, Anthony D. Kim

Bibliographic record

VenueMedical Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsHealth Sciences CentreCanada Research ChairsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsDosimeterImaging phantomLinear particle acceleratorNuclear medicineOptically stimulated luminescenceDosimetryMaterials scienceRadiation treatment planningMonte Carlo methodMedical physicsRadiation therapyBeam (structure)Dose profileBiomedical engineeringOpticsPhysicsMedicineRadiologyMathematics

Abstract

fetched live from OpenAlex

PURPOSE: This study aims to measure the surface dose on an anthropomorphic phantom for intensity-modulated radiation therapy (IMRT) plans treated in a 1.5 T magnetic resonance (MR)-Linac. Previous studies have used Monte Carlo programs to simulate surface dose and have recognized high surface dose as a potential limiting factor for the MR-Linac; however, to our knowledge surface dose measurement for clinical plans has not yet been published. Given the novelty of the MR-Linac, it is important to perform in vivo measurements of a potentially dose-limiting factor such as surface dose when moving forward for clinical use. METHODS: Optically stimulated luminescence dosimeters (OSLDs) were used on an anthropomorphic phantom. Intensity-modulated radiation therapy plans were generated to treat a near-surface breast tumor in the phantom. The tumor was treated with 2, 3, 5, 7, and 9 beam IMRT plans with a 1.5 T MR-Linac using a 7-MV photon beam. The plans were generated in a Monte Carlo treatment planning system (TPS) capable of modeling magnetic field effects. The surface dose was sampled in seven locations on the surface surrounding the planning target volume (PTV), and in two different OSLD configurations with the dosimeters measuring water equivalent depths of 0.16 and 0.64 mm. The TPS was used to estimate the doses at the OSLD locations. In addition, MR images were taken of a pork belly with and without an OSLD placed anteriorly placed to determine the effect of an OSLD on image fidelity. RESULTS: For the 3, 5, 7, and 9-beam configurations, surface doses were approximately half that of the prescription dose to the simulated tumor, although the magnitude of the skin dose relative to the prescription is certainly also dependent on individual patient anatomy. The general trend for both TPS and measurements was that the greater the number of beams, the lower the skin doses and dose readings; also, with increasing numbers of beams, doses at shallow depths become lower relative to deeper depths. The MR images showed that the presence of the OSLD did not induce clinically relevant geometric distortions or intensity differences. CONCLUSIONS: To our knowledge, this study is the first of its kind to experimentally measure the surface dose in an MR-Linac for IMRT plans. This study has explored the use of OSLDs to measure in vivo surface dose in a clinical setting. OSLDs may be used to measure skin dose clinically when there are concerns of skin radiation burns and near-surface toxicity. Optically stimulated luminescence dosimeters are promising devices for in vivo surface dosimetry in an MR-Linac.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.289
Teacher spread0.265 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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